Distribution network fault positioning method, system, equipment and medium

Abstract

The invention relates to the technical field of distribution network fault positioning, and discloses a distribution network fault positioning method, system and device and a medium, and the method comprises the steps: obtaining first data of a target power distribution network, and carrying out the first preprocessing of the first data; performing frequency domain feature extraction on the first data after the first preprocessing, and converting the frequency domain feature into a quantum state feature; establishing an entanglement relationship between the quantum states according to the quantum state characteristics, and converting the entanglement relationship into a Boolean satisfiability problem; solving the Boolean satisfiability problem to obtain a preliminarily positioned fault node; and carrying out confidence coefficient analysis on the preliminarily positioned fault node to obtain an optimized positioning result. According to the method, quantum state modeling and entanglement of fault signals are carried out through quantum state feature conversion and quantum bit and quantum gate operation, the expression ability and information density of fault features are effectively improved, and the accuracy of distribution network fault positioning is improved.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network fault location, and in particular to a distribution network fault location method, system, device and medium. Background Art

[0002] With the development of modern power grids, the structure of power systems has become increasingly complex, placing higher demands on the accuracy and real-time performance of fault location in distribution networks. Traditional fault location methods typically rely on centralized signal acquisition and processing. By analyzing the characteristics of signals such as voltage and current, signal processing methods such as wavelet transform and short-time Fourier transform are used to extract fault information. These methods perform well in small-scale, simple-structure distribution networks, but in more complex distributed distribution networks, the accuracy and efficiency of traditional methods are significantly limited. This limitation is mainly reflected in environments with large data volumes and complex fault characteristics. Traditional algorithms find it difficult to effectively cope with the high-dimensional characteristics of fault signals, leading to missed detections and false detections. In addition, since the fault feature extraction process relies on complex calculations, traditional algorithms typically perform poorly in terms of computational complexity and real-time responsiveness, making it difficult to meet the high real-time requirements of modern distribution networks.

[0003] Existing fault location methods are not convenient for processing complex and non-stationary signals in large-scale distributed distribution networks and are easily affected by data redundancy and noise. In addition, the algorithm complexity is high and it is difficult to meet the requirements of real-time processing. Summary of the Invention

[0004] In view of the above existing problems, this application is proposed.

[0005] Therefore, the present application provides a distribution network fault location method, system, equipment and medium, which can solve the problem that the existing fault location method is not convenient for processing complex and non-stationary signals in large-scale distributed distribution networks, is easily affected by data redundancy and noise, and has high algorithm complexity and is difficult to meet real-time processing requirements.

[0006] To solve the above technical problems, this application provides the following technical solutions:

[0007] In a first aspect, the present application provides a distribution network fault location method, comprising:

[0008] Acquiring first data of a target distribution network, and performing first preprocessing on the first data;

[0009] The first data is acquired through a distributed multi-pulse source;

[0010] Performing frequency domain feature extraction on the first data after the first preprocessing, and converting the frequency domain features into quantum state features;

[0011] establishing an entanglement relationship between quantum states according to the characteristics of the quantum states, and converting the entanglement relationship into a Boolean satisfiability problem;

[0012] Solve the Boolean satisfiability problem to obtain a preliminarily located fault node;

[0013] A confidence analysis is performed on the initially located fault node to obtain an optimized positioning result.

[0014] As a preferred solution of the distribution network fault location method described in the present application, wherein: extracting frequency domain features from the first data after the first preprocessing and converting the frequency domain features into quantum state features includes:

[0015] converting the first preprocessed first data into a frequency domain signal by using a fast Fourier transform;

[0016] Extract frequency domain features based on the obtained frequency domain signal;

[0017] The frequency and amplitude features in the extracted frequency domain features are mapped to the state space of the quantum bit, and the quantum state characteristics are defined.

[0018] As a preferred solution of the distribution network fault location method described in the present application, wherein: establishing an entanglement relationship between quantum states according to the quantum state characteristics includes:

[0019] Initialize the quantum state of the quantum state feature, and rotate the quantum bit corresponding to the amplitude of the frequency domain feature through a single quantum bit gate;

[0020] Exchange and rearrange the final quantum state after rotation;

[0021] Use the entanglement gate to entangle the quantum bits to form an entangled state;

[0022] Through the mapping rules, the quantum state and the entangled state after the exchange and rearrangement are logically associated to form a preliminary associated quantum state, that is, the entangled relationship.

[0023] As a preferred solution of the distribution network fault location method described in the present application, wherein: converting the entanglement relationship into a Boolean satisfiability problem includes:

[0024] Using the ground state measurement method to measure the preliminary correlated quantum state to obtain a Boolean variable;

[0025] Boolean variables are combined to obtain Boolean subformulas, which are Boolean satisfiability problems.

[0026] As a preferred solution of the distribution network fault location method described in the present application, wherein: the Boolean satisfiability problem is solved to obtain a preliminary located fault node including:

[0027] The Boolean variables are mapped to quantum bit states through quantum approximate optimization algorithms, and the quantum bit states are evaluated by using Pauli-Z and Pauli-X operators.

[0028] The Boolean subformula is converted into Hamiltonian through quantum approximate optimization algorithm;

[0029] The converted Hamiltonians are accumulated and the target Hamiltonian is constructed, and the mixed Hamiltonian is constructed through the Pauli-X operator;

[0030] The quantum state of the superposition state is evolved by applying the target Hamiltonian and the mixed Hamiltonian;

[0031] The final quantum state is measured using a computational-based measurement method;

[0032] Determine the node status based on a single measurement result;

[0033] When a single measurement result is equal to 1, it indicates that the node is a faulty node;

[0034] When the single measurement result is equal to 0, it means that the node is a normal node;

[0035] By measuring multiple times and recording the measurement results, a measurement result matrix is ​​constructed. The measurement result matrix is ​​the matrix of the initially located fault nodes.

[0036] This preferred solution can significantly improve the accuracy and efficiency of fault location. The application of quantum approximate optimization algorithms makes the solution process of Boolean satisfiability problems more efficient, and compared with traditional methods, it can obtain a preliminary location of the fault node in a shorter time. At the same time, by taking multiple measurements and recording the measurement results, and constructing a measurement result matrix, the reliability of positioning is further improved and the possibility of misjudgment is reduced. This method has broad application prospects in the field of distribution network fault location and can provide strong support for the stable operation of power systems.

[0037] As a preferred solution of the distribution network fault location method described in the present application, wherein: the confidence analysis of the initially located fault node is performed to obtain an optimized location result includes:

[0038] Performing statistical analysis and calculation on the failure probability data of the initially located faulty node to obtain a mean and a standard deviation;

[0039] Based on the obtained mean and standard deviation, calculate the deviation of the failure probability from the mean;

[0040] Use the box plot analysis method to identify nodes with deviations higher than the upper quartile and mark them as abnormal nodes;

[0041] The fault probability of the abnormal node is used as input, and the abnormal node is analyzed using the cluster analysis method. After the abnormal node is analyzed by the cluster analysis method, the result of the cluster analysis is output as the fault node to obtain the optimized positioning result.

[0042] As a preferred solution of the distribution network fault location method described in the present application, wherein: the obtaining of the first data of the target distribution network includes:

[0043] Deploy several distributed multi-pulse sources at substations, distribution line branch points, distribution transformers, and feeder switches in the target distribution network;

[0044] collecting voltage, current and phase data in real time according to the plurality of distributed multi-pulse sources;

[0045] The voltage, current and phase data collected in real time are denoised and the denoised voltage, current and phase data are time aligned.

[0046] In a second aspect, the present application provides a distribution network fault location system, comprising:

[0047] A data acquisition and processing module, configured to acquire first data of a target distribution network and perform first preprocessing on the first data;

[0048] The first data is acquired through a distributed multi-pulse source;

[0049] A feature extraction module, configured to extract frequency domain features from the first data after the first preprocessing, and convert the frequency domain features into quantum state features;

[0050] a conversion module, configured to establish an entanglement relationship between quantum states according to the characteristics of the quantum states, and convert the entanglement relationship into a Boolean satisfiability problem;

[0051] A node determination module is used to solve the Boolean satisfiability problem to obtain a preliminarily located fault node;

[0052] The optimization module is used to perform confidence analysis on the initially located fault node to obtain an optimized positioning result.

[0053] In a third aspect, the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described above when executing the computer program.

[0054] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described above when the computer program is executed by a processor.

[0055] Compared with the prior art, the beneficial effects of the present application are as follows: the present application proposes a distribution network fault location method, which obtains first data of the target distribution network and performs a first preprocessing on the first data; extracts frequency domain features from the first data after the first preprocessing, and converts the frequency domain features into quantum state features; establishes an entanglement relationship between quantum states based on the quantum state features, and converts the entanglement relationship into a Boolean satisfiability problem; solves the Boolean satisfiability problem to obtain a preliminary located fault node; and performs a confidence analysis on the preliminary located fault node to obtain an optimized location result. The present application performs quantum state modeling and entanglement of the fault signal through quantum state feature conversion and the use of quantum bits and quantum gate operations, effectively improving the expressive power and information density of the fault features and improving the accuracy of distribution network fault location. In addition, the present application performs preliminary location of the fault node through a quantum approximate optimization algorithm, which significantly reduces the computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0057] Figure 1 A flow chart of a method for locating a distribution network fault provided in one embodiment of the present application.

[0058] Figure 2 This is a diagram of the internal structure of an electronic device for a distribution network fault locating method provided in one embodiment of the present application. DETAILED DESCRIPTION

[0059] To make the above-mentioned purposes, features, and advantages of this application more clearly understood, the following detailed description of the specific embodiments of this application is given in conjunction with the accompanying drawings. It is obvious that the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of this application.

[0060] Example 1, reference Figure 1-Figure 2 , which is the first embodiment of the present application, provides a distribution network fault location method, including:

[0061] There are some problems in the existing related technologies, such as limited fault location accuracy, high data processing complexity and insufficient real-time responsiveness. In order to solve these problems.

[0062] This application provides a method that can effectively solve the above-mentioned problems. Next, we will explain in detail how to implement the distribution network fault location method in combination with multiple embodiments;

[0063] Figure 1 A method flow chart of a distribution network fault location method is shown, including:

[0064] S101, obtaining first data of a target distribution network, and performing first preprocessing on the first data;

[0065] It should be noted that in order to locate distribution network faults, it is necessary to obtain relevant data of the corresponding target distribution network. The first data includes but is not limited to voltage, current and phase data, which are collected in real time by a distributed multi-pulse source deployed in the target distribution network.

[0066] It should also be noted that the first preprocessing step mainly cleans the collected raw data, removes noise, and performs time alignment to ensure the accuracy and consistency of the data, providing a reliable basis for subsequent processing.

[0067] In an embodiment of the present application, the first data is acquired through a distributed multi-pulse source.

[0068] In an optional embodiment, the distributed multi-pulse sources can be deployed at locations including, but not limited to, substations, distribution line branch points, distribution transformers, and feeder switches within the target distribution network. By deploying distributed multi-pulse sources at these key locations, comprehensive and real-time electrical data from the distribution network can be collected, providing detailed data support for subsequent fault location.

[0069] In an optional embodiment, these distributed multi-pulse sources not only collect data in real time, but also possess high precision and sensitivity, enabling them to detect minute electrical changes and thus improving fault location accuracy. Furthermore, because data is collected in a distributed manner, even a failure at a single collection point will not affect data collection for the entire system, thus improving system reliability and stability.

[0070] In an optional embodiment, after acquiring the first data, the system performs a first preprocessing on the data. The preprocessing steps primarily include data cleaning, denoising, and time alignment. Data cleaning removes outliers and invalid values ​​from the data, ensuring data accuracy; denoising eliminates noise interference from the data, improving the signal-to-noise ratio; and time alignment ensures that data from different acquisition points remain consistent in time, facilitating subsequent data processing.

[0071] In an embodiment of the present application, obtaining first data of the target distribution network includes:

[0072] Deploy several distributed multi-pulse sources at substations, distribution line branch points, distribution transformers, and feeder switches in the target distribution network;

[0073] Real-time acquisition of voltage, current, and phase data based on several distributed multi-pulse sources;

[0074] The voltage, current and phase data collected in real time are denoised and the denoised voltage, current and phase data are time aligned.

[0075] Specifically, distributed multi-pulse sources are deployed at substations, distribution line branch points, distribution transformers, and feeder switches to collect voltage, current, and phase data in real time. Bandpass filters are used to denoise the real-time collected voltage, current, and phase data, and timestamp marking and difference technology are used to time-align the denoised voltage, current, and phase data.

[0076] In an optional embodiment, distributed multi-pulse sources are deployed at important nodes such as substations, distribution line branch points, distribution transformers, and feeder switches to achieve comprehensive coverage of the entire distribution network. This deployment structure enables the system to capture voltage, current, and phase change information at different locations when a fault occurs, thereby locating the source of the fault more quickly and accurately.

[0077] In an optional embodiment, a bandpass filter is used to denoise the voltage, current, and phase data, removing noise interference and making the fault characteristics clearer. The system records the acquisition time of each data point through a timestamp, providing a unified time reference for subsequent data synchronization and analysis.

[0078] The difference technology further utilizes timestamp information to perform time difference correction on the collected voltage, current, and phase data, eliminating the errors caused by time differences between different nodes. This effectively solves the time alignment problem in distributed data collection and provides a reliable foundation for multi-node data fusion and fault feature analysis. Furthermore, the collected data from all nodes is time-aligned;

[0079] It should be noted that this application ensures that different data sources are analyzed synchronously under the same time base, making the extraction results of frequency domain features more representative. This step is crucial in distributed fault detection systems, making subsequent quantum state conversion and Boolean solution processes more accurate, avoiding misjudgments and missed judgments due to time deviations.

[0080] S102, extracting frequency domain features from the first data after the first preprocessing, and converting the frequency domain features into quantum state features;

[0081] In an optional embodiment, frequency domain feature extraction can be implemented using a Fast Fourier Transform (FFT) algorithm, which converts time domain signals into frequency domain signals, thereby extracting frequency domain features from voltage, current, and phase data. These frequency domain features include the amplitude and phase information of different frequency components, which are crucial for identifying fault modes in distribution networks.

[0082] In an embodiment of the present application, extracting frequency domain features from the first data after the first preprocessing and converting the frequency domain features into quantum state features includes:

[0083] converting the first preprocessed first data into a frequency domain signal by using a fast Fourier transform;

[0084] Extract frequency domain features based on the obtained frequency domain signal;

[0085] The frequency and amplitude features in the extracted frequency domain features are mapped to the state space of the quantum bit, and the quantum state characteristics are defined.

[0086] Specifically, frequency domain features are extracted from the first data after the first preprocessing, and the frequency domain features are converted into quantum state features.

[0087] The pre-processed voltage, current and phase data are converted into frequency domain signals by using fast Fourier transform, and frequency domain features are extracted based on the obtained frequency domain signals;

[0088] Map the frequency and amplitude features extracted from the frequency domain features to the state space of the quantum bit and define the quantum state:

[0089]

[0090] Where, represents the quantum state, α k represents the amplitude information of each frequency component, |k> represents different quantum bit states, S represents the total number of frequency domain features, and k is the index of the frequency component.

[0091] It should be noted that the distribution network signal is converted from the time domain to the frequency domain through the fast Fourier transform to obtain the frequency component and amplitude information of the signal, which makes the conversion of data from the time domain to the frequency domain more efficient and suitable for real-time analysis of signals in complex and dynamic distribution network environments. In addition, the frequency domain characteristics contain the main vibration frequency of the signal and the corresponding amplitude changes, which can more sensitively reflect the characteristics of the power grid fault, thereby providing an accurate data basis for quantization processing, and the frequency domain characteristics are mapped to the quantum state.

[0092] It should also be noted that the efficient modeling of fault signals using the state space of quantum bits allows traditional frequency domain data to be stored in quantum bits in the form of quantum states, achieving high-density representation of signal data. The introduction of quantum states can greatly improve the ability to analyze complex signals through quantum superposition and entanglement characteristics.

[0093] It should also be noted that the conversion of frequency domain characteristics to quantum states enables fault characteristics to be more efficiently represented in the quantum bit state space, providing a basis for subsequent quantum computing steps. Moreover, the definition of quantum states and the state space mapping of quantum bits not only improve the efficiency of fault analysis, but also improve the adaptability to complex power grids.

[0094] S103, establishing an entanglement relationship between quantum states based on the characteristics of the quantum states, and converting the entanglement relationship into a Boolean satisfiability problem;

[0095] It should be noted that entanglement is a special relationship between quantum states, in which the combined state of two or more quantum states cannot be decomposed into a simple product of their individual states. In the embodiments of this application, entanglement relationships between different quantum states can be constructed through specific quantum algorithms or quantum gate operations. This entanglement relationship reflects the inherent connections and mutual influences between fault characteristics, providing a more refined and accurate information foundation for subsequent fault location.

[0096] In an optional embodiment, converting the entanglement relationship into a Boolean satisfiability problem (SAT problem) is an innovative method for combining quantum computing with classical computing. The SAT problem is a classic problem in computer science, the goal of which is to determine whether there is a set of variable assignments that makes a given Boolean expression true. In this application, by mapping the entanglement relationship of the quantum state to the constraints of the Boolean expression, the complex quantum state relationship can be converted into a SAT problem that can be solved on a classical computer.

[0097] It should be noted that this transformation not only leverages the advantages of quantum computing in solving complex problems, but also combines the efficiency of classical computing in solving specific types of problems, thereby achieving accurate and efficient distribution network fault location. By solving the transformed SAT problem, variable assignments that satisfy all constraints are obtained. These assignments correspond to the initially located fault nodes, providing key information for subsequent optimization.

[0098] In an embodiment of the present application, establishing an entanglement relationship between quantum states according to quantum state characteristics includes:

[0099] Initialize the quantum state of the quantum state feature, and rotate the quantum bit corresponding to the amplitude of the frequency domain feature through a single quantum bit gate;

[0100] Exchange and rearrange the final quantum state after rotation;

[0101] Use the entanglement gate to entangle the quantum bits to form an entangled state;

[0102] Through mapping rules, the quantum state after exchange and rearrangement and the entangled state are logically associated to form a preliminary associated quantum state, namely the entangled relationship.

[0103] In an embodiment of the present application, converting the entanglement relationship into a Boolean satisfiability problem includes:

[0104] Using the ground state measurement method to measure the preliminary correlated quantum state to obtain a Boolean variable;

[0105] Combining Boolean variables results in Boolean subformulas, which are Boolean satisfiability problems.

[0106] Specifically, the quantum state of the qubit is initialized, and the qubit is rotated by the amplitude corresponding to the frequency domain feature through a single qubit gate:

[0107]

[0108] θ k =2arccos(α k )

[0109] Where |0> represents the ground state of the quantum bit, θ k Represents the amplitude α k The associated rotation angle, surface

[0110] In an optional embodiment, the final quantum state after rotation is exchanged and rearranged by using a SWAP gate;

[0111] Use the entanglement gate to entangle the quantum bits to form an entangled state

[0112]

[0113] Where |00> represents the initial state of the quantum bit, and |11> represents the excited state of the quantum bit;

[0114] In an optional embodiment, the quantum state after exchange and rearrangement and the entangled state are logically associated through a mapping rule to form a preliminary associated quantum state, and the preliminary associated quantum state is measured using a ground state measurement method to obtain a Boolean variable;

[0115] In an optional embodiment, Boolean variables are combined to obtain Boolean sub-formulas.

[0116] In an optional embodiment, the frequency domain amplitude data of voltage and current are mapped to quantum states through a feature conversion method, which not only improves the dimension and information volume of data processing, but also provides a quantum state basis for subsequent quantum computing, thereby ensuring the accuracy of the quantization processing of distribution network data and laying the foundation for the detailed analysis and characterization of fault characteristics. The use of SWAP gates optimizes the correlation between quantum bits and ensures the accuracy of logical mapping.

[0117] It should be noted that this structural adjustment enables the present invention to maintain logical continuity when constructing Boolean formulas, thereby improving the efficiency of the system in solving Boolean formulas and reducing the positioning error caused by the failure of quantum bit correlation. The introduction of entangled states fully utilizes the non-locality of quantum bits and enhances the state expression capability between quantum bits. Through the entanglement relationship, the present invention can simultaneously reflect the potential correlation between multiple fault points and improve the accuracy of fault identification. For multi-node and multi-branch distribution network fault scenarios, the entangled state greatly expands the parallel computing capability of the system. By mapping the fault problem to a Boolean formula, it can be efficiently solved on a quantum computing platform.

[0118] S104, solving the Boolean satisfiability problem to obtain a preliminary located fault node;

[0119] In an optional embodiment, the initially located fault nodes are determined based on the results of solving a Boolean satisfiability problem. These nodes may be the locations where the fault occurred or the critical areas affected by the fault. In this embodiment of the present application, by solving the Boolean satisfiability problem, variable assignments that satisfy all constraints can be found. These assignments correspond to nodes in the distribution network that may have faults. Due to the efficiency and accuracy of the Boolean satisfiability problem solving process, it is possible to achieve initial and rapid location of distribution network faults, providing strong support for subsequent processing.

[0120] It should be noted that the initially located faulty nodes may contain some misidentifications or omissions, so further verification and optimization are required to improve the accuracy and reliability of the location. However, even with misidentifications or omissions, the initially located faulty nodes still have important reference value and can provide important clues and directions for subsequent troubleshooting and repair work.

[0121] In the embodiment of the present application, the Boolean satisfiability problem is solved, and the initially located fault nodes include:

[0122] The Boolean variables are mapped to quantum bit states through quantum approximate optimization algorithms, and the quantum bit states are evaluated by using Pauli-Z and Pauli-X operators.

[0123] The Boolean subformula is converted into Hamiltonian through quantum approximate optimization algorithm;

[0124] The converted Hamiltonians are accumulated and the target Hamiltonian is constructed, and the mixed Hamiltonian is constructed through the Pauli-X operator;

[0125] The quantum state of the superposition state is evolved by applying the target Hamiltonian and the mixed Hamiltonian;

[0126] The final quantum state is measured using a computational-based measurement method;

[0127] Determine the node status based on a single measurement result;

[0128] When a single measurement result is equal to 1, it indicates that the node is a faulty node;

[0129] When the single measurement result is equal to 0, it means that the node is a normal node;

[0130] By taking multiple measurements and recording the measurement results, a measurement result matrix is ​​constructed. The measurement result matrix is ​​the matrix of the initially located fault nodes.

[0131] Specifically, the Boolean variables are mapped to quantum bit states through a quantum approximate optimization algorithm, and the quantum bit states are evaluated by using Pauli-Z and Pauli-X operators. The Boolean subformulas are then converted into Hamiltonians through a quantum approximate optimization algorithm.

[0132] In an optional embodiment, the converted Hamiltonians are accumulated and a target Hamiltonian H is constructed. C and construct the mixed Hamiltonian H through the Pauli-X operator M :

[0133]

[0134] Where N is the number of quantum bits, Z i represents the Pauli-Z operator on the i-th quantum bit, Z o Z l represents the quantum state correlation between the oth and lth bits, G i represents the Pauli-X operator on the i-th quantum bit;

[0135] In an optional embodiment, the quantum state is initialized to form a uniform superposition state:

[0136]

[0137] Where |β0> represents the uniform superposition state after quantum state initialization, 2 Nrepresents the number of quantum states, i.e., the dimension of the state space of N bits, and v represents the state index;

[0138] In an alternative embodiment, by applying the target Hamiltonian H C and the mixed Hamiltonian H M Evolve the quantum state of the superposition state:

[0139]

[0140] Where |β1> represents the target Hamiltonian H C The quantum state after evolution, |β2> represents the mixed Hamiltonian H M The quantum state after evolution, |β nal > represents the final quantum state, Denotes the target Hamiltonian H C The evolution of the phase rotation operation, Denotes the mixed Hamiltonian H M The evolution of bit-flip operation;

[0141] In an optional embodiment, the final quantum state is measured by a computational basis measurement method:

[0142] Q=(z1,z2,...,z u )

[0143] Where Q represents a single measurement result, z u represents the u-th element in the binary vector;

[0144] In an optional embodiment, the state of the node is determined based on a single measurement result. When the single measurement result Q is equal to 1, it indicates that the node is a faulty node. When the single measurement result Q is equal to 0, it indicates that the node is a normal node.

[0145] The result of the measurement is the classical bit value, which can be directly obtained through the device's measurement read interface. This value is usually provided by the measurement module of the quantum computer, which directly determines whether the measurement result is 1 or 0, thereby determining the node state.

[0146] In an optional embodiment, multiple measurements are performed and the measurement results are recorded to construct a measurement result matrix, count the number of node failures, and calculate the failure probability:

[0147]

[0148] Where P represents the failure probability, f y represents the number of times the yth node is measured as a faulty state, and M′ represents the total number of measurements.

[0149] It should be noted that the Boolean satisfiability problem is solved by the quantum approximate optimization algorithm, the Boolean variables are mapped to quantum bit states, and the Pauli-Z and Pauli-X operators are used to operate the quantum bits to realize the conversion of Boolean subformulas into quantum subsystems. This can transform the distribution network fault location problem into a quantum state optimization problem, and approach the optimal solution through the rapid iterative evolution of the quantum state. The converted Hamiltonian is accumulated to construct the target Hamiltonian, and the Pauli-X operator is used to construct a mixed Hamiltonian, thereby providing a physical basis for the alternating evolution process of the quantum approximate optimization algorithm. The target Hamiltonian can represent the optimal solution in its lowest energy state, and the mixed Hamiltonian introduces quantum bit flipping between different solutions, so that the system can jump out of the local optimal solution and approach the global optimal solution. By constructing a reasonable Hamiltonian, the identification process of the faulty node is made more accurate and efficient, which helps to reduce the misjudgment rate in fault location.

[0150] It should be noted that in the quantum computing process, initializing the quantum bit state to a uniform superposition state can provide a uniform search basis for the system, that is, the state of each node has equal probability in the initial stage, which enables the present invention to fully explore the solution space in the early stage of quantum state evolution. Under the alternating action of the target Hamiltonian and the mixed Hamiltonian, the quantum state continues to evolve to approach the optimal solution. The target Hamiltonian makes the quantum bit tend to the optimal solution of the fault state, while the mixed Hamiltonian, through the action of the Pauli-X operator, causes the quantum bit to undergo ecological flipping, thereby increasing the flexibility of searching the solution space. In this way, the present invention can quickly switch between different solutions, and by measuring and recording the measurement results multiple times, constructing a fault probability matrix, and by counting the number of failures of each node in multiple measurements and calculating the failure probability, the failure risk of the node can be more stably evaluated.

[0151] S105: Perform confidence analysis on the initially located fault node to obtain an optimized positioning result.

[0152] In the embodiment of the present application, a confidence analysis is performed on the initially located fault node, and the optimized location result obtained includes:

[0153] Perform statistical analysis and calculation on the failure probability data of the initially located faulty nodes to obtain the mean and standard deviation;

[0154] Based on the obtained mean and standard deviation, calculate the deviation of the failure probability from the mean;

[0155] Use the box plot analysis method to identify nodes with deviations higher than the upper quartile and mark them as abnormal nodes;

[0156] The fault probability of the abnormal node is used as input, and the abnormal node is analyzed using the cluster analysis method. After the abnormal node is analyzed by the cluster analysis method, the result of the cluster analysis is output as the fault node to obtain the optimized positioning result.

[0157] Furthermore, by performing confidence analysis on the initially located fault nodes, the optimized positioning results include:

[0158] Perform statistical analysis and calculation on the node failure probability data to obtain the mean μ and standard deviation σ:

[0159] Based on the obtained mean and standard deviation, calculate the deviation R of the failure probability from the mean:

[0160]

[0161] Use the box plot analysis method to identify nodes with deviations higher than the upper quartile and mark them as abnormal nodes;

[0162] The fault probability of the abnormal node is used as input, and the abnormal node is analyzed using the cluster analysis method. After the abnormal node is analyzed by the cluster analysis method, the result of the cluster analysis is output as the fault node to obtain the fault node;

[0163] Analyzing abnormal nodes means using cluster analysis methods to divide abnormal nodes into "fault groups" and "normal groups", and obtaining fault nodes through fault groups.

[0164] By performing statistical analysis on the failure probability data of the initially located fault nodes, the mean and standard deviation of the failure probability of each node are obtained. The mean can reflect the average level of the failure probability of all nodes, and the standard deviation indicates the degree of dispersion of the failure probability. The deviation is further calculated based on the mean and standard deviation to quantify the degree of deviation of the failure probability of each node from the average level. The higher the deviation, the more obvious the failure probability of the node deviates from the normal range, which may be an abnormal fault node. This method can screen out nodes with significant deviations in failure probability, provide data support for further analysis of subsequent abnormal nodes, and significantly improve the accuracy of fault location. After obtaining the deviation data, the nodes are further screened by the box plot analysis method. Using the box plot analysis, nodes with higher deviations from the average failure probability can be intuitively identified to ensure that these potential outliers are not ignored. This visual statistical method can quickly screen out nodes with significant deviations in failure probability. Nodes with a relatively high probability of failure are selected to ensure higher confidence and accuracy in fault location. Nodes marked as abnormal are further input into the cluster analysis method for grouping. Unsupervised learning is used to divide abnormal nodes into "fault group" and "normal group" according to the similarity of their fault probabilities. Cluster analysis quantifies the failure probability of each node and groups similar nodes together, significantly improving the ability to distinguish abnormal nodes. The fault group nodes are output as the final fault nodes to ensure that the identification results are more reliable. After the cluster analysis is completed, the nodes in the fault group are the finally confirmed fault nodes and are used as the output results. Through this clustering grouping process, the present invention can accurately filter out the real fault nodes and eliminate those normal nodes caused by data deviation or accidental factors. The final confirmation of the fault nodes not only improves the reliability of fault location, but also significantly reduces the ineffective maintenance and resource waste caused by misjudgment.

[0165] In summary, the present application proposes a distribution network fault location method, which obtains first data of the target distribution network and performs a first preprocessing on the first data; extracts frequency domain features from the first data after the first preprocessing, and converts the frequency domain features into quantum state features; establishes an entanglement relationship between quantum states based on the quantum state features, and converts the entanglement relationship into a Boolean satisfiability problem; solves the Boolean satisfiability problem to obtain a preliminary located fault node; performs a confidence analysis on the preliminary located fault node to obtain an optimized location result. The present application performs quantum state modeling and entanglement of the fault signal through quantum state feature conversion and the use of quantum bits and quantum gate operations, which effectively improves the expressive power and information density of the fault features, and improves the accuracy of distribution network fault location. In addition, the present application performs preliminary location of the fault node through a quantum approximate optimization algorithm, which greatly reduces the computational complexity.

[0166] Example 2: In a preferred embodiment, the optimized positioning result can be displayed through a visual interface;

[0167] Specifically, receiving the optimized positioning results through the database for storage refers to receiving the node data after cluster analysis through the database, selecting the Excel file format to store the node data after receiving the node data, and naming the file, establishing a data table, and filling the cluster analysis results into the data table according to the corresponding fields. After the data entry is completed, save the file and store it in the company's shared folder.

[0168] Node data is received through the database and stored according to the results of cluster analysis. Compared with the traditional single data recording method, this method can realize hierarchical storage of data, which is convenient for rapid retrieval and data review of different categories. After the data is stored, the data is structured in Excel file format, which further improves the visibility and ease of operation of the data. Excel files can flexibly display fault node information and hierarchically manage information through specific fields such as node number and fault probability, providing efficient support for the company's fault management system. In addition, Excel files have good compatibility and are easy to integrate with other analysis software or tools, making data call and analysis more convenient. Through shared folders, relevant teams or personnel can access and obtain the latest node status data at any time, which is convenient for real-time monitoring of fault data and multi-department collaboration.

[0169] Furthermore, displaying through a visual interface means using the Matplotlib tool to create a fault probability distribution diagram, using a bar chart to display the node failure probability. The horizontal axis represents the node number, and the vertical axis represents the failure probability. Faulty nodes and normal nodes are marked with different colors.

[0170] Create a node status distribution diagram, use a pie chart to show the ratio of faulty nodes to normal nodes in all nodes, and use a scatter plot to show the failure probability of the node. The horizontal axis is the node number and the vertical axis is the failure probability. After marking different groups with colors, generate a visual chart and save it as an image.

[0171] Matplotlib's bar charts can intuitively display the failure probability of each node. Histograms display the failure probability as bar height, making the differences in failure probability between different nodes clear at a glance. The horizontal axis represents the node number, and the vertical axis represents the corresponding failure probability. Colors are used to distinguish between faulty and healthy nodes. This visual display method helps power system maintenance personnel quickly identify high-risk nodes and take preventive measures. The node status distribution diagram mainly provides a system-level fault overview. The pie chart shows the ratio of faulty nodes to healthy nodes. By comparing the ratios at a glance, maintenance personnel can quickly understand the overall health of the distribution network. This global information display allows managers to clearly see the number of normal and faulty nodes in the network, which facilitates the rational allocation of resources and the formulation of appropriate maintenance plans. A scatter plot displays the failure probability of each node, with the horizontal axis representing the node number and the vertical axis representing the failure probability, making it easy to view the probability distribution of different nodes. Charts generated by Matplotlib can be saved directly as image files, allowing these visualization results to be further used or displayed on multiple platforms.

[0172] Embodiment 3: This embodiment further provides a distribution network fault location system, including:

[0173] A data acquisition and processing module, configured to acquire first data of a target distribution network and perform first preprocessing on the first data;

[0174] The first data is acquired by a distributed multi-pulse source;

[0175] A feature extraction module, configured to extract frequency domain features from the first data after the first preprocessing, and convert the frequency domain features into quantum state features;

[0176] A conversion module is used to establish the entanglement relationship between quantum states based on the characteristics of the quantum states and convert the entanglement relationship into a Boolean satisfiability problem;

[0177] The node determination module is used to solve the Boolean satisfiability problem and obtain the preliminary located fault node;

[0178] The optimization module is used to perform confidence analysis on the initially located fault nodes to obtain optimized positioning results.

[0179] The above-mentioned unit modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0180] This embodiment also provides an electronic device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 2As shown. The electronic device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a distribution network fault location method is implemented. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the electronic device, or an external keyboard, touchpad or mouse.

[0181] This embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the following steps are implemented:

[0182] Acquiring first data of a target distribution network and performing first preprocessing on the first data;

[0183] The first data is acquired by a distributed multi-pulse source;

[0184] Performing frequency domain feature extraction on the first data after the first preprocessing, and converting the frequency domain features into quantum state features;

[0185] Establish entanglement relationships between quantum states based on their characteristics, and transform the entanglement relationships into Boolean satisfiability problems;

[0186] Solve the Boolean satisfiability problem to obtain the preliminary location of the fault node;

[0187] Perform confidence analysis on the initially located fault node to obtain the optimized positioning result.

[0188] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application, and all of these should be included in the scope of the claims of the present application.

[0189] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages.

[0190] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0191] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0192] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0193] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0194] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A distribution network fault location method, characterized in that: include: Acquiring first data of a target distribution network, and performing first preprocessing on the first data; The first data is acquired through a distributed multi-pulse source; Performing frequency domain feature extraction on the first data after the first preprocessing, and converting the frequency domain features into quantum state features; establishing an entanglement relationship between quantum states according to the characteristics of the quantum states, and converting the entanglement relationship into a Boolean satisfiability problem; Solve the Boolean satisfiability problem to obtain a preliminarily located fault node; A confidence analysis is performed on the initially located fault node to obtain an optimized positioning result.

2. A distribution network fault location method according to claim 1, characterized in that: The extracting frequency domain features from the first data after the first preprocessing and converting the frequency domain features into quantum state features comprises: converting the first preprocessed first data into a frequency domain signal by using a fast Fourier transform; Extract frequency domain features based on the obtained frequency domain signal; The frequency and amplitude features in the extracted frequency domain features are mapped to the state space of the quantum bit, and the quantum state characteristics are defined.

3. A distribution network fault location method according to claim 2, characterized in that: The establishing of an entanglement relationship between quantum states according to the quantum state characteristics includes: Initialize the quantum state of the quantum state feature, and rotate the quantum bit corresponding to the amplitude of the frequency domain feature through a single quantum bit gate; Exchange and rearrange the final quantum state after rotation; Use the entanglement gate to entangle the quantum bits to form an entangled state; Through the mapping rules, the quantum state and the entangled state after the exchange and rearrangement are logically associated to form a preliminary associated quantum state, that is, the entangled relationship.

4. A distribution network fault location method according to claim 3, characterized in that: Converting the entanglement relation into a Boolean satisfiability problem includes: Using the ground state measurement method to measure the preliminary correlated quantum state to obtain a Boolean variable; Boolean variables are combined to obtain Boolean subformulas, which are Boolean satisfiability problems.

5. A distribution network fault location method according to claim 4, characterized in that: The Boolean satisfiability problem is solved to obtain a preliminary located fault node including: The Boolean variables are mapped to quantum bit states through quantum approximate optimization algorithms, and the quantum bit states are evaluated by using Pauli-Z and Pauli-X operators. The Boolean subformula is converted into Hamiltonian through quantum approximate optimization algorithm; The converted Hamiltonians are accumulated and the target Hamiltonian is constructed, and the mixed Hamiltonian is constructed through the Pauli-X operator; The quantum state of the superposition state is evolved by applying the target Hamiltonian and the mixed Hamiltonian; The final quantum state is measured using a computational-based measurement method; Determine the node status based on a single measurement result; When a single measurement result is equal to 1, it indicates that the node is a faulty node; When the single measurement result is equal to 0, it means that the node is a normal node; By measuring multiple times and recording the measurement results, a measurement result matrix is ​​constructed. The measurement result matrix is ​​the matrix of the initially located fault nodes.

6. A distribution network fault location method according to claim 5, characterized in that: The performing of confidence analysis on the initially located fault node to obtain an optimized positioning result includes: Performing statistical analysis and calculation on the failure probability data of the initially located faulty node to obtain a mean and a standard deviation; Based on the obtained mean and standard deviation, calculate the deviation of the failure probability from the mean; Use the box plot analysis method to identify nodes with deviations higher than the upper quartile and mark them as abnormal nodes; The fault probability of the abnormal node is used as input, and the abnormal node is analyzed using the cluster analysis method. After the abnormal node is analyzed by the cluster analysis method, the result of the cluster analysis is output as the fault node to obtain the optimized positioning result.

7. A distribution network fault location method according to claim 6, characterized in that: The acquiring first data of the target distribution network includes: Deploy several distributed multi-pulse sources at substations, distribution line branch points, distribution transformers, and feeder switches in the target distribution network; collecting voltage, current and phase data in real time according to the plurality of distributed multi-pulse sources; The voltage, current and phase data collected in real time are denoised and the denoised voltage, current and phase data are time aligned.

8. A distribution network fault location system, applying the method according to any one of claims 1 to 7, characterized in that: include: A data acquisition and processing module, configured to acquire first data of a target distribution network and perform first preprocessing on the first data; The first data is acquired through a distributed multi-pulse source; A feature extraction module, configured to extract frequency domain features from the first data after the first preprocessing, and convert the frequency domain features into quantum state features; a conversion module, configured to establish an entanglement relationship between quantum states according to the characteristics of the quantum states, and convert the entanglement relationship into a Boolean satisfiability problem; A node determination module is used to solve the Boolean satisfiability problem to obtain a preliminarily located fault node; The optimization module is used to perform confidence analysis on the initially located fault node to obtain an optimized positioning result.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a distribution network fault locating method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a distribution network fault locating method according to any one of claims 1 to 7 are implemented.

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