Segment positioning method based on similarity of zero sequence current derivative and magnetic field trajectory

Abstract

This invention belongs to the field of power system distribution network relay protection technology, and provides a segment location method based on the similarity of zero-sequence current derivative and magnetic field trajectory. The method includes the following steps: Step 1, obtaining the phase magnetic field at each magnetic field sensor on each feeder and the zero-sequence current flowing through the corresponding position of the magnetic field sensor; Step 2, calculating the zero-sequence current derivative and zero-sequence horizontal magnetic field component flowing through the corresponding position of each magnetic field sensor; Step 3, constructing a normalized dynamic trajectory at each magnetic field sensor; Step 4, calculating the similarity of the dynamic trajectories of adjacent magnetic field sensors. This invention identifies fault segments by constructing a two-dimensional dynamic trajectory of the normalized zero-sequence current derivative and zero-sequence horizontal magnetic field component at each magnetic field sensor and calculating the similarity of trajectories between adjacent sensors. This invention significantly improves the accuracy and reliability of location under complex operating conditions through comprehensive judgment based on trajectory similarity.

Description

Technical Field

[0001] This invention belongs to the field of relay protection technology for power system distribution networks, specifically a segment location method based on the similarity between the zero-sequence current derivative and the magnetic field trajectory. Background Technology

[0002] Single-phase grounding faults are the most frequent and widespread type of fault in distribution networks, accounting for 60% to 85% of all distribution line faults, according to statistics. However, due to the complex topology and numerous branches of distribution networks, and the weak fault current and complex and variable transient characteristics after a single-phase grounding fault, existing fault location methods still face many challenges in practical applications. Currently widely used methods such as fault indicators have low location accuracy due to limitations in principle and performance, and are prone to misjudgment and missed detection. Location methods based on transient signals are easily affected by disturbance factors and line distance limitations, resulting in large deviations between the location results and the actual fault location, and long location times. Location methods based on zero-sequence components or signal injection often require direct contact with overhead lines, which is not only inconvenient for installation and maintenance but also poses certain safety risks. In recent years, some studies have explored non-contact fault detection methods based on spatial electromagnetic field induction, finding that the horizontal magnetic field under the line is basically proportional to the zero-mode current, and fault location is achieved by analyzing the characteristics of the magnetic field trajectory. However, existing methods mostly focus on the analysis of single characteristics of magnetic field amplitude or trajectory shape, and have not fully explored the intrinsic relationship between the transient changes of zero-sequence current and the spatial distribution of magnetic field.

[0003] To address this, the present invention proposes a segment location method based on the similarity between the zero-sequence current derivative and the dynamic trajectory of the horizontal magnetic field. By constructing a two-dimensional dynamic trajectory of the zero-sequence current derivative and the zero-sequence horizontal magnetic field, and calculating the similarity between the trajectories of adjacent sensors, the method achieves accurate identification of fault segments without direct contact with overhead bare conductors, providing a new technical approach for non-contact fault location. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a segment location method based on the similarity between the zero-sequence current derivative and the magnetic field trajectory. This addresses the issues in existing technologies, which often focus on analyzing single features such as magnetic field amplitude or trajectory shape, and fail to fully explore the intrinsic relationship between transient changes in zero-sequence current and the spatial distribution of the magnetic field.

[0005] The segment localization method based on the similarity between the zero-sequence current derivative and the magnetic field trajectory includes the following steps:

[0006] Step 1: Data Acquisition: Obtain the magnetic field of each phase at each magnetic field sensor along each feeder. The zero-sequence current flowing through the corresponding position of the magnetic field sensor ,in, Here are the three phases of the feeder, and k is the feeder number. Number the magnetic field sensor;

[0007] Step 2: Calculate the derivative of the zero-sequence current flowing through the corresponding position of each magnetic field sensor. and zero-sequence horizontal magnetic field component ;

[0008] Step 3: Construct normalized data at each magnetic field sensor. The dynamic trajectory;

[0009] Step 4: Calculate the similarity of the dynamic trajectories of adjacent magnetic field sensors. ;

[0010] Step 5, Detection Criteria: Then k * The magnetic field sensor of the feeder i * and i * +1 indicates the faulty section.

[0011] Preferably, the specific process of step 1 is as follows:

[0012] Step 1.1: Based on the tower structure and the installation location of the magnetic field sensor, obtain the distance from each phase feeder to the installation location of the magnetic field sensor. ;

[0013] Step 1.2: Obtain the phase magnetic field information of each magnetic field sensor on each feeder:

[0014] ;

[0015] In the formula, r is the vacuum permeability. j This represents the distance from each phase feeder to the installation location of the magnetic field sensor. This refers to the zero-sequence current flowing through the corresponding position of each magnetic field sensor on each feeder.

[0016] Preferably, step 2 is specifically performed as follows:

[0017] Step 2.1: Calculate the derivative of the zero-sequence current flowing through each magnetic field sensor at the corresponding location on each feeder. :

[0018] ;

[0019] In the formula, k is the feeder number. Number the magnetic field sensor. It is the zero-sequence current;

[0020] Step 2.2: Calculate the zero-sequence component of each phase magnetic field of the magnetic field sensor on each feeder line using the symmetrical component method. :

[0021] ;

[0022] In the formula, , and These are the magnetic fields of phases A, B, and C of the first magnetic field sensor on the first feeder. The zero-sequence magnetic field of phase A, the first magnetic field sensor of feeder 1, is given. For phases B and C, the zero-sequence current calculation formula is the same as that of phase A.

[0023] Step 2.3: Based on the tower structure and the installation location of the magnetic field sensor, obtain the distance between each phase feeder and the magnetic field sensor. and the angle formed by each phase feeder and the magnetic field sensor The zero-sequence horizontal magnetic field of each feeder was calculated. :

[0024] ;

[0025] In the formula, Zero-order horizontal magnetic field For each phase magnetic field, , and These are the cosine values ​​of the angles formed between the A, B, and C phases of the feeder and the magnetic field sensor, respectively.

[0026] Preferably, the specific process of step 3 is as follows:

[0027] Step 3.1: Calculate the derivative of the zero-sequence current flowing through the corresponding position of each magnetic field sensor on each feeder. and magnetic field Normalization is performed, and the normalized zero-sequence current derivative is used as the x-axis of the dynamic trajectory, and the normalized magnetic field is used as the y-axis of the dynamic trajectory; the formula is as follows:

[0028] ;

[0029] In the formula, and These are the normalized values ​​of the zero-sequence current derivative sequence and the magnetic field sequence, respectively.

[0030] Step 3.2: Construct the two-dimensional dynamic trajectory equation:

[0031] ;

[0032] In the formula, The derivative of the zero-sequence current after normalization With zero-sequence horizontal magnetic field The dynamic trajectory.

[0033] Preferably, the formula for calculating the similarity of the dynamic trajectories of adjacent magnetic field sensors in step 4 is as follows:

[0034] ;

[0035] In the formula, The similarity of the dynamic trajectories of adjacent magnetic field sensors. From arrive Relevance and Trajectories The m-th sampling point and trajectory The nth sampling point, The distance between the two points is the Euclidean distance.

[0036] Preferably, the criterion formula for step 5 is: Then k * The magnetic field sensor of the feeder i * and i * +1 represents the faulty section, where, Indicates taking The independent variable.

[0037] A segment localization system based on the similarity between zero-sequence current derivative and magnetic field trajectory is applicable to the aforementioned segment localization method based on the similarity between zero-sequence current derivative and magnetic field trajectory, including:

[0038] The data acquisition module is used to acquire the magnetic field of each phase at each magnetic field sensor on each feeder and the zero-sequence current flowing through the corresponding position of the magnetic field sensor;

[0039] The zero-sequence current derivative calculation module is used to calculate the zero-sequence current derivative flowing through the corresponding positions of each magnetic field sensor;

[0040] The zero-sequence horizontal magnetic field component calculation module is used to calculate the zero-sequence horizontal magnetic field component at the corresponding position of each magnetic field sensor.

[0041] The dynamic trajectory construction module is used to construct the normalized dynamic trajectory of the zero-sequence current derivative and the zero-sequence horizontal magnetic field component at each magnetic field sensor.

[0042] The similarity calculation module is used to calculate the similarity of the dynamic trajectories of adjacent magnetic field sensors.

[0043] The fault section determination module is used to compare the similarity with a preset threshold. When the similarity of the dynamic trajectories of adjacent magnetic field sensors is less than the preset threshold, the section between the adjacent magnetic field sensors is determined to be a fault section.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. This invention identifies fault sections by constructing two-dimensional dynamic trajectories of the normalized zero-sequence current derivative and the zero-sequence horizontal magnetic field component at each magnetic field sensor, and calculating the similarity of trajectories between adjacent sensors. This method fully explores the intrinsic correlation between transient changes in zero-sequence current and the spatial distribution of the magnetic field, rather than relying solely on single features such as magnetic field amplitude or trajectory shape. Compared to existing positioning methods based on transient signals, which are susceptible to disturbances and line distance limitations, resulting in significant positioning deviations, this invention significantly improves positioning accuracy and reliability under complex operating conditions through comprehensive judgment based on trajectory similarity.

[0046] 2. This invention introduces the zero-sequence current derivative as a dimension for constructing the dynamic trajectory. Compared to methods that only analyze the zero-sequence current or magnetic field amplitude, it is more sensitive to changes in the transient characteristics of the fault. Simulation results show that under typical fault conditions such as different initial phase angles (0°, 30°, 60°) and different transition resistances (50Ω, 240Ω, 500Ω), this invention can accurately locate the fault section. The trajectory similarity of adjacent magnetic field sensors in the fault section is significantly greater than that in other sections, and the judgment criteria are clear and definite, effectively overcoming the technical difficulty of accurately identifying high-resistance grounding faults in distribution networks due to their weak characteristics and the difficulty of accurate identification by traditional methods.

[0047] 3. This invention can locate fault sections without direct contact with bare overhead conductors. It acquires magnetic field information for each phase using magnetic field sensors deployed on towers, and calculates the zero-sequence horizontal magnetic field component based on the tower structure and sensor installation location. Compared to traditional fault indicators and other methods that require direct contact with the line, are inconvenient to install and maintain, and pose safety risks, this invention uses a non-contact measurement method, reducing construction difficulty and maintenance costs, while avoiding the safety hazards associated with direct contact with high-voltage lines.

[0048] 4. This invention organically combines the transient change characteristics (derivative) of zero-sequence current with the spatial distribution characteristics (horizontal component) of the magnetic field to construct a two-dimensional dynamic trajectory and perform similarity analysis. This technical approach breaks through the limitations of existing methods that focus on single feature analysis, providing a new technical approach for non-contact fault location. Furthermore, this method has a clear principle, requiring only a magnetic field sensor to complete data acquisition without relying on voltage transformers for signal injection, and has the advantages of simple implementation and wide applicability. Attached Figure Description

[0049] Figure 1 This is a flowchart of the fault detection process of the present invention;

[0050] Figure 2 This is the zero-sequence magnetic field vector diagram of the present invention;

[0051] Figure 3 This is a schematic diagram of the 10kV ungrounded system model of the present invention;

[0052] Figure 4 This is a schematic diagram of the zero-sequence current derivative and the dynamic trajectory of the zero-sequence horizontal magnetic field of each sensor in this invention. Detailed Implementation

[0053] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0054] Example: This invention provides a segment location method based on the similarity between the zero-sequence current derivative and the magnetic field trajectory, such as... Figure 1 As shown, it includes the following steps:

[0055] Step 1: Data Acquisition: Obtain the magnetic field of each phase at each magnetic field sensor along each feeder. The zero-sequence current flowing through the corresponding position of the magnetic field sensor ,in, Here are the three phases of the feeder, and k is the feeder number. Number the magnetic field sensor;

[0056] Step 2: Calculate the derivative of the zero-sequence current flowing through the corresponding position of each magnetic field sensor. and zero-sequence horizontal magnetic field component ;

[0057] Step 3: Construct normalized data at each magnetic field sensor. The dynamic trajectory;

[0058] Step 4: Calculate the similarity of the dynamic trajectories of adjacent magnetic field sensors. ;

[0059] Step 5, Detection Criteria: Then k * The magnetic field sensor of the feeder i * and i * +1 indicates the faulty section.

[0060] As can be seen from the above, this method is no longer limited to the single feature analysis of magnetic field amplitude or trajectory shape, but fully explores the intrinsic relationship between the transient change (derivative) of zero-sequence current and the spatial distribution of zero-sequence horizontal magnetic field. In steps 2 to 4, a normalized two-dimensional dynamic trajectory is constructed and the trajectory similarity between adjacent sensors is quantified, thereby achieving accurate identification of fault sections in step 5. This technical approach effectively overcomes the shortcomings of traditional transient signal location methods, such as susceptibility to disturbances and line distance, weak and difficult-to-identify high-resistance grounding fault characteristics, and inconvenient installation and maintenance of contact detection methods. Simulation verification shows that this method can accurately locate fault sections under different fault initial phase angles and transition resistance conditions. It has outstanding advantages such as high positioning accuracy, strong anti-interference ability, and can be implemented without contacting overhead lines, providing a reliable new technical solution for non-contact distribution network fault location.

[0061] Preferably, the specific process of step 1 is as follows:

[0062] Step 1.1: Based on the tower structure and the installation location of the magnetic field sensor, obtain the distance from each phase feeder to the installation location of the magnetic field sensor. ;

[0063] Step 1.2: Obtain the phase magnetic field information of each magnetic field sensor on each feeder:

[0064] ;

[0065] In the formula, r is the vacuum permeability. j This represents the distance from each phase feeder to the installation location of the magnetic field sensor. This refers to the zero-sequence current flowing through the corresponding position of each magnetic field sensor on each feeder.

[0066] As can be seen from the above, step 1, by obtaining the distance from each phase feeder to the magnetic field sensor in advance based on the tower structure and sensor installation position in step 1.1, and combining it with step 1.2 to calculate the magnetic field information of each phase at each magnetic field sensor based on the vacuum permeability and distance parameters, achieves accurate acquisition of magnetic field data without direct contact with the overhead line. This process makes full use of spatial geometric relationships and electromagnetic field principles, avoiding the inconvenience of installation and maintenance and safety risks caused by traditional contact measurement. At the same time, it provides a reliable data foundation for the accurate calculation of the zero-sequence horizontal magnetic field component and the construction of dynamic trajectory, thus ensuring the accuracy and feasibility of the entire positioning method from the source.

[0067] Preferably, step 2 is specifically performed as follows:

[0068] Step 2.1: Calculate the derivative of the zero-sequence current flowing through each magnetic field sensor at the corresponding location on each feeder. :

[0069] ;

[0070] In the formula, k is the feeder number. Number the magnetic field sensor. It is the zero-sequence current;

[0071] Step 2.2: Calculate the zero-sequence component of each phase magnetic field of the magnetic field sensor on each feeder line using the symmetrical component method. :

[0072] ;

[0073] In the formula, , and These are the magnetic fields of phases A, B, and C of the first magnetic field sensor on the first feeder. The zero-sequence magnetic field of phase A, the first magnetic field sensor of feeder 1, is given. For phases B and C, the zero-sequence current calculation formula is the same as that of phase A.

[0074] Step 2.3: Based on the tower structure and the installation location of the magnetic field sensor, obtain the distance between each phase feeder and the magnetic field sensor. and the angle formed by each phase feeder and the magnetic field sensor The zero-sequence horizontal magnetic field of each feeder was calculated. :

[0075] ;

[0076] In the formula, Zero-order horizontal magnetic field For each phase magnetic field, , and These are the cosine values ​​of the angles formed between the A, B, and C phases of the feeder and the magnetic field sensor, respectively.

[0077] As can be seen from the above, step 2, by calculating the zero-sequence current derivative in step 2.1, can effectively amplify the transient change characteristics of the fault moment, making it easier to capture the weak high-resistance grounding fault signal. At the same time, step 2.2 uses the symmetrical component method to accurately extract the zero-sequence magnetic field component from each phase magnetic field, and combines it with the phase distance and angle information obtained in step 2.3 based on the tower structure and sensor installation position to calculate the zero-sequence horizontal magnetic field component, thus organically integrating non-contact magnetic field measurement with fault transient analysis. This process overcomes the shortcomings of traditional methods that are easily interfered with and insensitive to high-resistance grounding when relying only on the zero-sequence current amplitude or a single magnetic field feature. It provides more discriminative feature parameters for subsequent dynamic trajectory construction and similarity discrimination, significantly improving the accuracy of fault location and anti-interference capability.

[0078] Preferably, the specific process of step 3 is as follows:

[0079] Step 3.1: Calculate the derivative of the zero-sequence current flowing through the corresponding position of each magnetic field sensor on each feeder. and magnetic field Normalization is performed, and the normalized zero-sequence current derivative is used as the x-axis of the dynamic trajectory, and the normalized magnetic field is used as the y-axis of the dynamic trajectory; the formula is as follows:

[0080] ;

[0081] In the formula, and These are the normalized values ​​of the zero-sequence current derivative sequence and the magnetic field sequence, respectively.

[0082] Step 3.2: Construct the two-dimensional dynamic trajectory equation:

[0083] ;

[0084] In the formula, The derivative of the zero-sequence current after normalization With zero-sequence horizontal magnetic field The dynamic trajectory.

[0085] As shown above, step 3, through step 3.1, normalizes the zero-sequence current derivative and zero-sequence horizontal magnetic field at each magnetic field sensor, eliminating the differences in dimensions and numerical ranges between different sensors, making the data at each measurement point comparable, and constructing a two-dimensional dynamic trajectory using the normalized current derivative as the horizontal axis and the magnetic field as the vertical axis; step 3.2 further establishes a unified dynamic trajectory equation, transforming the originally isolated time series data into a trajectory graphic with spatial morphological characteristics. The beneficial effects of this process are: the normalization process effectively avoids the influence of amplitude deviation caused by differences in the installation position and line parameters of each sensor, making the trajectory shape fully reflect the intrinsic relationship between the transient change of zero-sequence current and the spatial distribution of magnetic field, while the construction of the two-dimensional dynamic trajectory completely preserves the temporal information and correlation of the fault transient process, extending the fault characteristics from a single time domain to the phase plane space, providing a stable and highly discriminative basis for quantifying the similarity of adjacent sensor trajectories in step 4, and significantly enhancing the positioning reliability under high-resistance grounding and weak fault characteristic conditions.

[0086] Preferably, the formula for calculating the similarity of the dynamic trajectories of adjacent magnetic field sensors in step 4 is as follows:

[0087] ;

[0088] In the formula, The similarity of the dynamic trajectories of adjacent magnetic field sensors. From arrive Relevance and Trajectories The m-th sampling point and trajectory The nth sampling point, The distance between the two points is the Euclidean distance.

[0089] As shown above, step 4 calculates the similarity of the dynamic trajectories of adjacent magnetic field sensors using a correlation function based on Euclidean distance. Specifically, it comprehensively considers the Euclidean distance between all sampling points on the two trajectories and transforms it into a similarity value that reflects the overall correlation. The beneficial effects of this step are: it quantifies the morphological differences of the two-dimensional dynamic trajectories between adjacent sensors into clear numerical indicators, so that the characteristics of dissimilar trajectories on both sides of the fault section (i.e., significantly low similarity) can be clearly presented through numerical values, making it easier to distinguish from healthy sections; at the same time, the similarity measurement method based on Euclidean distance is sensitive to the overall shape and trend of the trajectory, and can effectively resist the interference of local sampling noise and small amplitude fluctuations, providing a stable and highly discriminative quantitative basis for the fault section determination based on threshold comparison in step 5, thereby significantly improving the accuracy and robustness of the positioning method under complex transient conditions.

[0090] Preferably, the criterion formula for step 5 is: Then k * The magnetic field sensor of the feeder i * and i * +1 represents the faulty section, where, Indicates taking The independent variable.

[0091] As shown above, the criterion formula for step 5 is: take the maximum value among the similarities of the dynamic trajectories of all adjacent magnetic field sensors, and determine the corresponding feeder number and sensor number as the faulty section (i.e., if...). Then the first The first feeder The and the first The section between the magnetic field sensors is the fault section. The beneficial effects of this step are: using the maximum value criterion eliminates the need for preset empirical thresholds, completely avoiding misjudgment or omission caused by improper threshold selection, and possessing adaptability and universality; at the same time, the criterion is simple in principle and computationally efficient, and can quickly locate the section with the highest similarity (i.e., the most significant difference in trajectory shape) from all adjacent sensor pairs, which is completely consistent with the physical law that the trajectories on both sides of the fault section are dissimilar, while the trajectories of the healthy section have low similarity. Simulation verification also shows that the criterion can accurately locate the fault under different fault initial phase angles and transition resistance conditions, significantly improving the reliability and engineering practicality of the method.

[0092] Application example:

[0093] As shown in the attached diagram. Figure 3 The simulation model of the 10kV resonant grounding system is shown. Four feeders L1~L4 are set up, representing an overhead line, an overhead line, a cable line, and a cable line, respectively. Magnetic field sensors are installed on the overhead line towers. The installation locations and vector synthesis results of the magnetic field sensors are shown below. Figure 2 As shown, the fault is assumed to occur at 6km along the L1 overhead line, with phase A grounding fault. The initial phase angle β = 0° and the grounding resistance is 50Ω. The line parameters are shown in Table 1.

[0094] Table 1 Overhead Line Parameters

[0095]

[0096] Plot the zero-sequence current derivatives and dynamic trajectories of the induced zero-sequence horizontal magnetic field for each magnetic field sensor after a malfunction, and calculate the trajectory similarity between adjacent sensors. Dynamic trajectory such as Figure 4 As shown, according to Figure 4 The dynamic trajectories show that the upstream trajectory of the faulty feeder is relatively smooth and full, while the downstream trajectory is not smooth and full, indicating a dissimilarity between the two. Furthermore, the dynamic trajectory of the healthy feeder is irregular and also dissimilar to that of the faulty feeder. To verify the adaptability of the segment location detection method based on magnetic field direction, the proposed method was used to calculate the similarity between the zero-sequence current derivative and the zero-sequence horizontal magnetic field dynamic trajectory of adjacent sensors. Simulations were conducted under various typical fault conditions, and the results are shown in Table 2.

[0097] Table 2 Simulation verification results

[0098]

[0099] The table above shows the simulation verification results of the section location method based on magnetic field direction under various typical fault conditions. The table is grouped according to the fault location (different sections of feeder L1 or L2), fault initial phase angle β, and transition resistance Rg, and lists the calculation results of the similarity between the zero-sequence current derivative and the dynamic trajectory of the zero-sequence horizontal magnetic field of adjacent sensor pairs. From the data results, the similarity between adjacent magnetic field sensors in the faulty section is significantly greater than that in other sections. Therefore, the maximum value is used as the fault criterion. The table verifies that the method can accurately locate the faulty section under different initial phase angles and transition resistances.

[0100] A segment localization system based on the similarity between zero-sequence current derivative and magnetic field trajectory is applicable to the aforementioned segment localization method based on the similarity between zero-sequence current derivative and magnetic field trajectory, including:

[0101] The data acquisition module is used to acquire the magnetic field of each phase at each magnetic field sensor on each feeder and the zero-sequence current flowing through the corresponding position of the magnetic field sensor;

[0102] The zero-sequence current derivative calculation module is used to calculate the zero-sequence current derivative flowing through the corresponding positions of each magnetic field sensor;

[0103] The zero-sequence horizontal magnetic field component calculation module is used to calculate the zero-sequence horizontal magnetic field component at the corresponding position of each magnetic field sensor.

[0104] The dynamic trajectory construction module is used to construct the normalized dynamic trajectory of the zero-sequence current derivative and the zero-sequence horizontal magnetic field component at each magnetic field sensor.

[0105] The similarity calculation module is used to calculate the similarity of the dynamic trajectories of adjacent magnetic field sensors.

[0106] The fault section determination module is used to compare the similarity with a preset threshold. When the similarity of the dynamic trajectories of adjacent magnetic field sensors is less than the preset threshold, the section between the adjacent magnetic field sensors is determined to be a fault section.

[0107] As can be seen from the above, this system achieves fully automated processing from data acquisition, feature extraction, trajectory construction, similarity quantification to fault determination through the organic collaboration of the data acquisition module, zero-sequence current derivative calculation module, zero-sequence horizontal magnetic field component calculation module, dynamic trajectory construction module, similarity calculation module, and fault section determination module. The system solidifies the method steps of this invention into a functionally clear modular architecture, with clear division of labor among modules and efficient data flow. This facilitates integration and deployment in embedded devices or smart terminals and significantly reduces the need for manual intervention by on-site maintenance personnel. More importantly, the fault section determination module uses a preset threshold comparison criterion, which can quickly and accurately locate adjacent sensor pairs with similarity below the threshold, effectively avoiding the uncertainty brought about by manual experience judgment. This provides a systematic, standardized, and engineered technical solution for non-contact section location of single-phase grounding faults in distribution networks.

[0108] This application provides an electronic device applicable to the above-mentioned segment location method based on the similarity between zero-sequence current derivative and magnetic field trajectory, including:

[0109] Memory, used to store computer programs and data;

[0110] A processor is used to run system programs.

[0111] This application provides a computer storage medium applicable to the above-mentioned segment location method based on the similarity between zero-sequence current derivative and magnetic field trajectory, and performs hierarchical confidentiality management of the above-mentioned system and data in accordance with confidentiality management requirements.

[0112] Those skilled in the art will understand that embodiments of this application can be provided as a system or a computer program product. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take 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.) containing computer-usable program code.

[0113] This application is described with reference to flowchart illustrations and / or block diagrams of devices (systems) and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0114] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0115] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0116] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0117] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0118] Computer-readable media include both permanent and non-permanent, removable and non-removable media, which can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0119] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, article, or apparatus that includes that element.

[0120] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A segment location method based on the similarity between the zero-sequence current derivative and the magnetic field trajectory, characterized in that, Includes the following steps: Step 1: Data Acquisition: Obtain the magnetic field of each phase at each magnetic field sensor along each feeder. The zero-sequence current flowing through the corresponding position of the magnetic field sensor ,in, Here are the three phases of the feeder, and k is the feeder number. Number the magnetic field sensor; Step 2: Calculate the derivative of the zero-sequence current flowing through the corresponding position of each magnetic field sensor. and zero-sequence horizontal magnetic field component ; Step 3: Construct normalized data at each magnetic field sensor. The dynamic trajectory; Step 4: Calculate the similarity of the dynamic trajectories of adjacent magnetic field sensors. ; Step 5, Detection Criteria: Then k * The magnetic field sensor of the feeder i * and i * +1 indicates the faulty section.

2. The segment location method based on the similarity between zero-sequence current derivative and magnetic field trajectory as described in claim 1, characterized in that: The specific process of step 1 is as follows: Step 1.1: Based on the tower structure and the installation location of the magnetic field sensor, obtain the distance from each phase feeder to the installation location of the magnetic field sensor. ; Step 1.2: Obtain the phase magnetic field information of each magnetic field sensor on each feeder: ； In the formula, r is the vacuum permeability. j This represents the distance from each phase feeder to the installation location of the magnetic field sensor. This refers to the zero-sequence current flowing through the corresponding position of each magnetic field sensor on each feeder.

3. The segment location method based on the similarity between the zero-sequence current derivative and the magnetic field trajectory as described in claim 1, characterized in that: The specific process of step 2 is as follows: Step 2.1: Calculate the derivative of the zero-sequence current flowing through each magnetic field sensor at the corresponding location on each feeder. : ； In the formula, k is the feeder number. Number the magnetic field sensor. It is the zero-sequence current; Step 2.2: Calculate the zero-sequence component of each phase magnetic field of the magnetic field sensor on each feeder line using the symmetrical component method. : ； In the formula, , and These represent the magnetic fields of phases A, B, and C of the first magnetic field sensor on the first feeder. The zero-sequence magnetic field of phase A, the first magnetic field sensor of feeder 1, is given. For phases B and C, the zero-sequence current calculation formula is the same as that of phase A. Step 2.3: Based on the tower structure and the installation location of the magnetic field sensor, obtain the distance between each phase feeder and the magnetic field sensor. and the angle formed by each phase feeder and the magnetic field sensor The zero-sequence horizontal magnetic field of each feeder was calculated. : ； In the formula, Zero-order horizontal magnetic field For each phase magnetic field, , and These are the cosine values ​​of the angles formed between the A, B, and C phases of the feeder and the magnetic field sensor, respectively.

4. The segment location method based on the similarity between zero-sequence current derivative and magnetic field trajectory as described in claim 1, characterized in that: The specific process of step 3 is as follows: Step 3.1: Calculate the derivative of the zero-sequence current flowing through the corresponding position of each magnetic field sensor on each feeder. and magnetic field Normalization is performed, and the normalized zero-sequence current derivative is used as the x-axis of the dynamic trajectory, and the normalized magnetic field is used as the y-axis of the dynamic trajectory; the formula is as follows: ； In the formula, and These are the normalized values ​​of the zero-sequence current derivative sequence and the magnetic field sequence, respectively. Step 3.2: Construct the two-dimensional dynamic trajectory equation: ； In the formula, The derivative of the zero-sequence current after normalization With zero-sequence horizontal magnetic field The dynamic trajectory.

5. The segment location method based on the similarity between zero-sequence current derivative and magnetic field trajectory as described in claim 1, characterized in that: The formula for calculating the similarity of the dynamic trajectories of adjacent magnetic field sensors in step 4 is as follows: ； In the formula, The similarity of the dynamic trajectories of adjacent magnetic field sensors. From arrive Relevance and Trajectories The m-th sampling point and trajectory The nth sampling point, The distance between the two points is the Euclidean distance.

6. The segment location method based on the similarity between zero-sequence current derivative and magnetic field trajectory as described in claim 1, characterized in that: The criterion formula for step 5 is as follows: Then k * The magnetic field sensor of the feeder i * and i * +1 represents the faulty section, where, Indicates taking The independent variable.

7. A segment positioning system based on the similarity between the zero-sequence current derivative and the magnetic field trajectory, characterized in that, The segment localization method based on the similarity between zero-sequence current derivative and magnetic field trajectory as described in any one of claims 1-6 includes: The data acquisition module is used to acquire the magnetic field of each phase at each magnetic field sensor on each feeder and the zero-sequence current flowing through the corresponding position of the magnetic field sensor; The zero-sequence current derivative calculation module is used to calculate the zero-sequence current derivative flowing through the corresponding positions of each magnetic field sensor; The zero-sequence horizontal magnetic field component calculation module is used to calculate the zero-sequence horizontal magnetic field component at the corresponding position of each magnetic field sensor. The dynamic trajectory construction module is used to construct the normalized dynamic trajectory of the zero-sequence current derivative and the zero-sequence horizontal magnetic field component at each magnetic field sensor. The similarity calculation module is used to calculate the similarity of the dynamic trajectories of adjacent magnetic field sensors. The fault section determination module is used to compare the similarity with a preset threshold. When the similarity of the dynamic trajectories of adjacent magnetic field sensors is less than the preset threshold, the section between the adjacent magnetic field sensors is determined to be a fault section.

Images