Method and apparatus for locating fault in electric power system, and processor and storage medium

Abstract

A method and apparatus for locating a fault in an electric power system, and a processor and a storage medium, which relate to the technical field of fault detection in electric power systems. The method comprises: acquiring basic data of an electric power system, wherein the basic data is used for indicating electrical parameters of a plurality of monitoring points in the electric power system (S101); on the basis of the basic data, determining graph structure information of the electric power system, wherein the graph structure information is used for representing the positions of the plurality of monitoring points in the electric power system and connection relationships between the plurality of monitoring points (S102); and inputting the graph structure information into a target detection model to perform analysis, so as to obtain a fault location in the electric power system, wherein the target detection model is obtained by performing training using historical basic data of the electric power system, and the historical basic data is used for indicating historical electrical parameters of the plurality of monitoring points in the electric power system (S103). The method solves the technical problem of low efficiency in locating a fault in an electric power system.

Description

Methods, devices, processors, and storage media for locating power system faults

[0001] This application claims priority to Chinese Patent Application No. 202411544823.0, filed on October 31, 2024, entitled "Method, Apparatus, Processor and Storage Medium for Locating Power System Faults", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of power system fault detection technology, and in particular to a method, apparatus, processor and storage medium for locating power system faults. Background Technology

[0003] Currently, the power system, as one of the fundamental infrastructures of modern society, bears the important responsibility of providing a stable and reliable power supply to various users. However, the power system is highly complex, encompassing multiple stages such as power generation, transmission, and distribution, and involving a large number of devices and lines. This complexity makes the power system susceptible to various faults during operation, such as line short circuits and equipment damage. The stability of the power system is the foundation for its efficient operation. When a fault occurs in the system, if it cannot be located and repaired in a timely manner, it may lead to the escalation of the fault or even system collapse. Therefore, in the power system, accurately and quickly locating the fault point is crucial to ensuring the stable operation of the power system.

[0004] In related technologies, fault diagnosis methods based on human experience, such as fault tree analysis, fault mode and effects analysis, and event tree analysis, rely on the experience and knowledge level of operators, thus their accuracy may be limited. Therefore, there is a technical problem of low efficiency in locating power system faults.

[0005] There is currently no effective solution to the aforementioned technical problem of low efficiency in locating power system faults. Summary of the Invention

[0006] This invention provides a method, apparatus, processor, and storage medium for locating power system faults, thereby at least addressing the technical problem of low efficiency in locating power system faults.

[0007] According to one aspect of the present invention, a method for locating a power system fault is provided. The method may include: acquiring basic data of the power system, wherein the basic data indicates electrical parameters of multiple monitoring points in the power system; determining graph structure information of the power system based on the basic data, wherein the graph structure information represents the locations of multiple monitoring points in the power system and the connection relationships between the multiple monitoring points; inputting the graph structure information into a target detection model for analysis to obtain the fault location of the power system, wherein the target detection model is trained using historical basic data of the power system, and the historical basic data indicates historical electrical parameters of multiple monitoring points in the power system.

[0008] Optionally, based on the basic data, the graph structure information of the power system is determined, including: based on the basic data, determining each monitoring point of the power system, wherein the monitoring point is used to indicate the location for monitoring the operating status of the power system; and based on the monitoring points, determining the graph structure information of the power system.

[0009] Optionally, based on the monitoring points, the graph structure information of the power system is determined, including: based on the monitoring points, determining the monitoring point connection data, wherein the monitoring point connection data is used to indicate the connection information between the monitoring points; and constructing the monitoring points and the monitoring point data into the graph structure information of the power system.

[0010] Optionally, the method for locating power system faults may further include: acquiring historical basic data of the power system; and using the historical basic data to train an initial detection model to obtain a target detection model.

[0011] Optionally, historical baseline data is input into the initial detection model for training to obtain the target detection model, including: determining the historical fault types of power system faults corresponding to the historical baseline data; determining the adjustment parameters of the initial detection model based on the historical fault types and historical baseline data; and training the initial detection model based on the adjustment parameters and historical baseline data to obtain the target detection model.

[0012] Optionally, the method for locating power system faults further includes: evaluating an initial detection model and determining the evaluation result of the initial detection model, wherein the evaluation result is used to indicate the accuracy of the initial detection model in locating power system faults; and determining the initial detection model as the target detection model in response to the evaluation result being greater than the evaluation result threshold.

[0013] According to another aspect of the present invention, a power system fault location device is also provided. The device may include: an acquisition unit for acquiring basic data of the power system, wherein the basic data indicates the electrical parameters of multiple monitoring points in the power system; a determination unit for determining graph structure information of the power system based on the basic data, wherein the graph structure information represents the locations of multiple monitoring points in the power system and the connection relationships between the multiple monitoring points; and an analysis unit for inputting the graph structure information into a target detection model for analysis to obtain the fault location of the power system, wherein the target detection model is trained using historical basic data of the power system, and the historical basic data indicates the historical electrical parameters of multiple monitoring points in the power system.

[0014] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein, when the program is run by a processor, it controls the device where the storage medium is located to execute the power system fault location method of the present invention.

[0015] According to another aspect of the present invention, a processor is also provided. The processor is used to run a program, wherein the program executes the power system fault location method according to the embodiments of the present invention.

[0016] According to another aspect of the present invention, a computer program product is also provided. The program product includes computer instructions that, when executed by a processor, implement the power system fault location method according to the embodiments of the present invention.

[0017] In this embodiment of the invention, basic data of the power system is acquired, wherein the basic data is used to indicate the electrical parameters of multiple monitoring points in the power system; based on the basic data, the graph structure information of the power system is determined, wherein the graph structure information is used to represent the location of multiple monitoring points in the power system and the connection relationship between multiple monitoring points; the graph structure information is input into a target detection model for analysis to obtain the fault location of the power system, wherein the target detection model is trained using historical basic data of the power system, and the historical basic data is used to indicate the historical electrical parameters of multiple monitoring points in the power system. In other words, this invention establishes the graph structure information of the power system through basic data of the power system, inputs the graph structure information into a target detection model for analysis, thereby determining the fault location of the power system, thus solving the technical problem of low efficiency in locating power system faults and achieving the technical effect of improving the efficiency of locating power system faults. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0019] Figure 1 is a flowchart of a power system fault location method according to an embodiment of the present invention;

[0020] Figure 2 is a flowchart of a novel power system method according to an embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of a power system fault location device according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, functional component, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, functional components, or devices.

[0024] According to an embodiment of the present invention, an embodiment of a method for locating power system faults is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] Figure 1 is a flowchart of a power system fault location method according to an embodiment of the present invention. As shown in Figure 1, the method may include the following steps:

[0026] Step S101: Obtain basic data of the power system.

[0027] In the technical solution provided by step S101 of the present invention, the basic data is used to indicate the electrical parameters of multiple monitoring points in the power system.

[0028] In this embodiment, basic data of the power system is acquired. This basic data can include electrical parameters of the power system such as current, voltage, and power.

[0029] Alternatively, sensors can be installed at key nodes of the power system to acquire basic data of the power system. This is merely an example and does not limit the specific method for acquiring basic data of the power system.

[0030] Step S102: Based on the basic data, determine the graph structure information of the power system.

[0031] In the technical solution provided by step S102 of the present invention, the graph structure information is used to represent the location of multiple monitoring points in the power system and the connection relationship between the multiple monitoring points.

[0032] In this embodiment, after obtaining the basic data of the power system in step S101, the graph structure information of the power system is determined based on the basic data. The graph structure information can also be simply referred to as the graph structure.

[0033] Optionally, each monitoring point of the power system is determined from the basic data, and the monitoring points are used as nodes in the graph structure, and the connection information between the nodes is used as edges in the graph structure, thereby establishing a graph structure model and determining the graph structure information.

[0034] For example, a power system can be abstracted as a graph structure G = (V, E), where V can be used to represent the set of nodes, representing the monitoring points in the power system; and E is the set of edges, representing the connections between the monitoring points.

[0035] Optionally, by analyzing and calculating the attributes of nodes and edges in the power system, more accurate heuristic information can be provided for fault location, thereby improving the efficiency and accuracy of fault location. The heuristic information can include distances between nodes, etc.

[0036] Step S103: Input the graph structure information into the target detection model for analysis to obtain the fault location of the power system.

[0037] In the technical solution provided by step S103 of the present invention, the target detection model is trained using historical basic data of the power system, and the historical basic data is used to indicate the historical electrical parameters of multiple monitoring points in the power system.

[0038] In this embodiment, after determining the graph structure information of the power system in step S102, the graph structure information is input into the target detection model for analysis to obtain the fault location of the power system.

[0039] Optionally, historical baseline data of the power system can be input into the initial detection model for training, thereby determining the target detection model. The initial detection model may include a fault location algorithm.

[0040] For example, for each node v in the graph structure information i ∈V, assigned the following attributes: electrical parameter vector P i =(I i V i W i …), where I i It can be used to represent node v i The current value, V i It is the voltage value, W i These are power values ​​and other relevant electrical parameters. Position coordinates (x...) i ,y i ,z i (If it is in three-dimensional space, or on a two-dimensional plane it is (x) i ,y i This is used to calculate heuristic information such as distances between nodes. For each edge e ij =(v i ,v j For each ∈ E, determine the following attribute: weight w ij The initial detection model can be calculated as follows: Considering electrical characteristics, the initial detection model can be expressed using the following formula (1):

[0041] E ij =k1×|I i -I j |+k2×|V i -V j |+k3×|W i -W J |+… (1)

[0042] K1, K2, K3, ... can be used to represent adjustment coefficients. The adjustment coefficients can be adjusted according to the actual situation. By inputting historical basic data into the above initial detection model for training, the adjustment coefficients are continuously determined, thereby obtaining the target detection model.

[0043] It should be noted that the above embodiments can be implemented using a power system fault location device.

[0044] In steps S101 to S103 of this invention, basic data of the power system is acquired, wherein the basic data is used to indicate the electrical parameters of multiple monitoring points in the power system; based on the basic data, the graph structure information of the power system is determined, wherein the graph structure information is used to represent the location of multiple monitoring points in the power system and the connection relationship between multiple monitoring points; the graph structure information is input into a target detection model for analysis to obtain the fault location of the power system, wherein the target detection model is trained using historical basic data of the power system, and the historical basic data is used to indicate the historical electrical parameters of multiple monitoring points in the power system. In other words, this embodiment of the invention establishes graph structure information of the power system through basic data of the power system, inputs the graph structure information into a target detection model for analysis, thereby determining the fault location of the power system, thus solving the technical problem of low efficiency in locating power system faults and achieving the technical effect of improving the efficiency of locating power system faults.

[0045] The method described in this embodiment will be further described below.

[0046] As an optional embodiment, step S102, based on the basic data, determines the graph structure information of the power system, including: based on the basic data, determining each monitoring point of the power system, wherein the monitoring point is used to indicate the location for monitoring the operating status of the power system; and based on the monitoring points, determining the graph structure information of the power system.

[0047] In this embodiment, various monitoring points of the power system are determined from basic data. For example, the location information of power system monitoring points is extracted from the basic data. This is merely an example and does not limit the specific method for determining the monitoring points of the power system.

[0048] Optionally, the graph structure information of the power system can be determined based on each monitoring point. For example, each monitoring point can be abstracted into a set of nodes, that is, a set of nodes V, representing the monitoring points in the power system. A graph structure model of the power system can then be constructed based on the set of nodes, and the graph structure information of the power system can be determined based on the graph structure model.

[0049] As an optional embodiment, determining the graph structure information of the power system based on monitoring points includes: determining monitoring point connection data based on monitoring points, wherein the monitoring point connection data is used to indicate the connection information between monitoring points; and constructing the monitoring points and monitoring point data into the graph structure information of the power system.

[0050] In this embodiment, monitoring point connection data is determined based on each monitoring point. For example, the monitoring point connection data is determined by determining the connection information between each monitoring point.

[0051] Optionally, the monitoring points and their data can be constructed as a graph structure of the power system. That is, the power system can be abstracted into a graph structure G = (V, E), where V is a set of nodes representing the monitoring points in the power system, and E is a set of edges representing the connection information between the monitoring points.

[0052] As an optional implementation method, historical basic data is input into an initial detection model for training to obtain a target detection model, including: determining the historical fault types of power system faults corresponding to the historical basic data; determining the adjustment parameters of the initial detection model based on the historical fault types and historical basic data; and training the initial detection model based on the adjustment parameters and historical basic data to obtain the target detection model.

[0053] In this embodiment, the historical fault types of the power system corresponding to the historical basic data are determined, wherein the fault types can be short circuit faults, ground faults, etc.

[0054] Optionally, the adjustment parameters of the initial detection model are determined based on historical fault types and historical basic data. The adjustment parameters can also be called adjustment coefficients, which are used to adjust the weight of different electrical parameters in the calculation of electrical characteristic factors. For example, in the aforementioned formula (1), K1, K2, K3, etc. represent adjustment coefficients. The adjustment coefficients can be determined based on the specific characteristics of the power system, the fault type, and the analysis results of actual data.

[0055] Optionally, the initial detection model can be trained based on the adjustment parameters and historical baseline data to obtain the target detection model. For example, if the difference in current parameters is determined to be very important for judging the electrical connection status between two monitoring points, the current parameter value can be increased so that the current difference accounts for a larger proportion of the electrical characteristic factors.

[0056] Optionally, adjustment coefficients can also be used to adjust the weighting of voltage parameter differences. The value can be adjusted based on the importance of voltage parameters in fault diagnosis within the actual power system. If voltage changes have a significant indicative effect on faults, the weighting of voltage changes can be appropriately increased. This is mainly used to adjust the weighting of power parameter differences. When changes in power parameters have a significant impact on determining the fault location, the weighting of power parameters can be increased.

[0057] Optionally, by determining different adjustment parameters, the adjustment coefficients can be determined based on the specific characteristics of the power system, the type of fault, and the analysis results of actual data. By adjusting these coefficients, the target detection model can be better adapted to different power system scenarios, improving the accuracy of fault location.

[0058] As an optional embodiment, the method for locating power system faults further includes: evaluating an initial detection model and determining an evaluation result of the initial detection model, wherein the evaluation result is used to indicate the accuracy of the initial detection model in locating power system faults; and determining the initial detection model as a target detection model in response to the evaluation result being greater than an evaluation result threshold.

[0059] In this embodiment, the initial detection model is evaluated to determine the evaluation result. For example, different evaluation metrics are used to evaluate the initial detection model. This is merely an example and does not limit the specific method for evaluating the initial detection model.

[0060] Optionally, when the evaluation result is greater than the evaluation result threshold, it indicates that the accuracy of the initial detection model has met the detection requirements. Based on this, the initial detection model can be determined as the target detection model.

[0061] Optionally, the fault location results can be verified and evaluated to continuously improve the model and algorithm, thereby enhancing the accuracy and reliability of fault location.

[0062] It should be noted that the above embodiments can be implemented using a power system fault location device.

[0063] In this embodiment, basic data of the power system is acquired, wherein the basic data indicates the electrical parameters of multiple monitoring points in the power system; based on the basic data, the graph structure information of the power system is determined, wherein the graph structure information represents the location of multiple monitoring points in the power system and the connection relationships between multiple monitoring points; the graph structure information is input into a target detection model for analysis to obtain the fault location of the power system, wherein the target detection model is trained using historical basic data of the power system, and the historical basic data indicates the historical electrical parameters of multiple monitoring points in the power system. In other words, this embodiment of the invention establishes graph structure information of the power system through basic data of the power system, inputs the graph structure information into a target detection model for analysis, thereby determining the fault location of the power system, thus solving the technical problem of low efficiency in locating power system faults and achieving the technical effect of improving the efficiency of locating power system faults.

[0064] The technical solutions of the embodiments of the present invention will be illustrated below with reference to preferred embodiments.

[0065] Currently, the power system, as one of the fundamental infrastructures of modern society, bears the important responsibility of providing a stable and reliable power supply to various users. However, the power system is highly complex, encompassing multiple stages such as power generation, transmission, and distribution, and involving a large number of devices and lines. This complexity makes the power system susceptible to various faults during operation, such as line short circuits and equipment damage. The stability of the power system is the foundation for its efficient operation. When a fault occurs in the system, if it cannot be located and repaired in a timely manner, it may lead to the escalation of the fault or even system collapse. Therefore, in the power system, accurately and quickly locating the fault point is crucial to ensuring the stable operation of the power system.

[0066] In related technologies, fault diagnosis methods based on human experience, such as fault tree analysis, fault mode and effects analysis, and event tree analysis, rely on the experience and knowledge level of operators, thus their accuracy may be limited. Therefore, there is a technical problem of low efficiency in locating power system faults. Currently, no effective solution has been proposed to address this problem of low efficiency in locating power system faults.

[0067] However, this invention proposes a novel power system model and method for power system fault location. By collecting electrical parameters such as current, voltage, and power, and determining the location coordinates of nodes, the power system is abstracted into a graph structure. The fault location of the power system is determined using the constructed model. By adjusting the algorithm parameters and adjustment coefficients in the model, the performance of fault location is optimized, thereby solving the technical problem of low efficiency in locating power system faults and achieving the technical effect of improving the efficiency of locating power system faults.

[0068] The embodiments of the present invention will be further described below.

[0069] Figure 2 is a flowchart of a novel power system method according to an embodiment of the present invention. The analysis method includes the following steps:

[0070] Step S201: Collect electrical parameters such as current, voltage, and power of the power system, and determine the location coordinates of the nodes.

[0071] In this embodiment, sensors are installed at key nodes of the power system to collect electrical parameters such as current, voltage, and power, and to determine the location coordinates of the nodes.

[0072] Step S202: Based on the collected data, construct a power system model and determine the attributes of nodes and edges.

[0073] In this embodiment, the power system is abstracted as a graph structure G = (V, E), where V is a set of nodes representing monitoring points in the power system, and E is a set of edges representing connections between monitoring points.

[0074] Optionally, for each node v i ∈V, assigned the following attributes: electrical parameter vector P i =(I i V i W i …), where I i It is node v i The current value, V i It is the voltage value, W i These are power values ​​and other relevant electrical parameters.

[0075] Optionally, the position coordinates (x i ,y i ,z i If it's in three-dimensional space, or on a two-dimensional plane, it's (x... i ,y i This is used to calculate heuristic information such as distances between nodes. For each edge e ij =(v i ,v j For each ∈ E, determine the following attribute: weight w ij , calculated using the aforementioned formula (1).

[0076] Optionally, in the aforementioned power system model, K1, K2, K3, etc., are adjustment coefficients used to adjust the weights of different electrical parameters in the calculation of electrical characteristic factors.

[0077] Optionally, the adjustment factor is used to adjust the weight of the current parameter difference in the electrical characteristic factors. For example, if the difference in current parameters is considered very important for determining the electrical connection status between two monitoring points, the value can be increased to give the current difference a greater weight in the electrical characteristic factors.

[0078] Optionally, the adjustment factor can also be used to adjust the weighting of voltage parameter differences. The value can be adjusted based on the importance of voltage parameters in fault diagnosis within the actual power system. If voltage changes have a significant indicative effect on fault location, the factor can be appropriately increased. It is mainly used to adjust the weighting of power parameter differences. When changes in power parameters have a significant impact on determining the fault location, the factor can be increased.

[0079] Optionally, these adjustment coefficients can be determined based on the specific characteristics of the power system, the type of fault, and the analysis results of actual data. By adjusting these coefficients, the model can be better adapted to different power system scenarios, improving the accuracy of fault location.

[0080] Alternatively, considering physical distance, if the node's position coordinates are (X,Y,Z), then the physical distance can be expressed using the following formula (2):

[0081] Optionally, a distance factor can be further set. Introducing a matrix to comprehensively consider electrical characteristics and distance, the electrical characteristic M... E Element M E (i,j) represents node v i and v j The relationship between their electrical characteristics can be determined based on the electrical characteristic factor E. ij Configure the distance matrix M. D , where M D (i,j) represents node v i and v j The distance relationship between them can be determined based on the distance factor D. ij Configure the settings.

[0082] Optionally, the electrical characteristics and distance relationships between nodes in a power system are complex and diverse. By introducing matrices, these relationships can be represented in a structured manner. The electrical characteristic matrix can integrate the differences in multiple electrical parameters such as current, voltage, and power, comprehensively reflecting the electrical connections between nodes. The distance matrix can accurately represent the spatial relationships between nodes, providing important clues about distance for fault location.

[0083] Optionally, the matrix form makes storing and processing various relationships more convenient. Matrix operations can be used to quickly calculate the edge weights between nodes, improving the efficiency of the algorithm. At the same time, matrices also facilitate data analysis and pattern recognition, helping to discover potential fault characteristics and patterns.

[0084] Alternatively, the weighting function can be expressed using the following formula (3):

[0085] w ij =f(M E (i,j),M D (i,j)) (3)

[0086] Optionally, the model can be further refined using a linear function, which can be expressed using the following formula (4):

[0087] f(M E (i,j),M D (i,j))=(M E (i,j) γ +1)×(M D (i,j) δ +1) (4)

[0088] Here, γ and δ are adjustment parameters used to balance the contributions of electrical characteristics and distance to edge weights.

[0089] Step S203: Apply the constructed model to the fault location algorithm to determine the target detection model.

[0090] In this embodiment, the constructed model can be combined with various fault location algorithms, and is particularly suitable for fault location based on ant colony optimization (ACO). In ACO, ants select paths based on edge weights and differences in electrical parameters between nodes, thereby gradually approaching the fault point.

[0091] Optionally, by analyzing and calculating the attributes of nodes and edges in the model, more accurate heuristic information can be provided for fault location, thereby improving the efficiency and accuracy of fault location.

[0092] Optionally, the introduction of matrices provides a foundation for combining with more complex algorithms. For example, advanced algorithms such as matrix factorization and graph neural networks can be used to analyze and process electrical characteristic matrices and distance matrices, further improving the accuracy and efficiency of fault location.

[0093] Optionally, the matrix can be adjusted as an important parameter during model optimization. By adjusting the element values, structure, and parameters of the matrix, different weight allocation schemes can be explored to find the optimal fault location strategy. Simultaneously, the matrix facilitates parallel computing and distributed processing, providing an effective solution for fault location in large-scale power systems.

[0094] Step S204: Use the target detection model to detect faults in the power system.

[0095] In this embodiment, the currently acquired power system data is input into the target detection model to perform fault detection on the power system.

[0096] Optionally, in real-world power systems, the relationships between nodes are often not simple linear relationships. Introducing matrices can better capture these complex nonlinear relationships, thereby improving the accuracy of the model's description of the power system. For example, by adjusting the element values ​​of the electrical characteristic matrix and the distance matrix, flexible modeling can be performed according to different power system scenarios and fault types, making the model more adaptable.

[0097] Optionally, it can be adapted to systems of different sizes: the matrix can be easily expanded and adjusted for power systems of different sizes and structures. Whether it is a small local power network or a large, complex power system, the relationships between nodes can be accurately described by constructing a suitable matrix, ensuring that the model can effectively locate faults under various conditions.

[0098] Optionally, fault location in power systems is a key aspect of ensuring the stable operation of power systems. Existing methods mainly include the following: offline fault location methods, online fault location methods, and other methods.

[0099] Optionally, offline fault location methods include: pulse signal injection method and fault indicator method. The pulse signal injection method involves injecting a high-voltage pulse signal onto the disconnected faulty line and then detecting the fault point along the line. Specifically, the method uses DC segmentation and AC point location.

[0100] Alternatively, the pulse signal injection method requires power outage and disconnection of the faulty line, while also requiring the provision of vehicle power, which may cause some inconvenience in actual operation.

[0101] Alternatively, the fault indicator method involves installing a ground fault indicator on the line to monitor the line current in real time. When a fault is detected, the fault indicator will flip its label, emit light, or send an alarm signal.

[0102] Alternatively, while the fault indicator method allows for online monitoring, it has higher installation and maintenance costs and requires regular inspection and replacement of the fault indicators.

[0103] Optionally, online fault location methods include: S-signal injection method and partial discharge method. The S-signal injection method injects a 220Hz signal onto the low-voltage side of the PT and detects the fault point along the path.

[0104] Alternatively, the S-signal injection method has a higher capital cost and may increase the workload of line maintenance, while also posing certain safety hazards.

[0105] Alternatively, the partial discharge method locates faults by monitoring partial discharge signals in the cable. Partial discharge occurs when the cable insulation material ages, is damaged, or has defects. Specific sensors and measuring equipment are used to monitor this in real time, detecting the intensity and location of the discharge signal to determine the fault area.

[0106] Alternatively, the partial discharge method requires high precision from the monitoring equipment and may be affected by environmental factors.

[0107] Alternatively, the cable reflection method locates the fault based on the signal reflection characteristics within the cable. A voltage or current signal is applied to one end, and the reflected signal is received at the other end. The transmission time and intensity changes are measured, and the approximate distance to the fault is calculated based on the signal transmission speed and reflection time.

[0108] Alternatively, the accuracy of the cable reflection method may be affected by factors such as cable length, load resistance, and fault type.

[0109] Optionally, the time-domain reflectometry method is used to analyze the transmission line characteristics and reflected signals in the cable. A pulse signal is sent at one end, and the time and amplitude of the reflected signal are measured to calculate the fault distance and type.

[0110] Alternatively, for signals propagating over long distances, the time-domain reflectometry method is affected by more factors affecting wave speed, making it difficult to estimate accurately and leading to increased positioning errors.

[0111] Alternatively, infrared thermal imaging uses an infrared thermal imager to scan the cable and detect heat changes at the fault point. Cable faults are often accompanied by localized temperature rises, and the thermal imager can visualize the heat distribution to pinpoint the fault location.

[0112] Alternatively, infrared thermography is affected by ambient temperature and cable heat dissipation conditions, and may not accurately reflect the actual temperature of the fault point.

[0113] Alternatively, other methods include fault location and traveling wave location.

[0114] Alternatively, fault location can be calculated by measuring electrical quantities (such as voltage and current) between the fault point and the measuring terminal.

[0115] Alternatively, fault location may result in inaccurate distance measurement due to variations in circuit length and waveform distortion.

[0116] Alternatively, traveling wave ranging utilizes the propagation characteristics of traveling waves generated by a fault in the transmission line for ranging.

[0117] Alternatively, traveling wave ranging requires power outages for installation and maintenance, which presents challenges in terms of power supply; its accuracy is also affected by external factors such as terrain, weather conditions, and changes in line parameters.

[0118] In summary, existing methods have certain drawbacks in terms of cost and location effectiveness. Based on this, the present invention provides a novel power system model that can more effectively support power fault location, improve the accuracy and speed of fault location, and enhance adaptability to power systems of different scales and types.

[0119] In this embodiment, by collecting electrical parameters such as current, voltage, and power, and determining the location coordinates of nodes, the power system is abstracted into a graph structure. The fault location of the power system is determined using the constructed model. By adjusting the algorithm parameters and adjustment coefficients in the model, the performance of fault location is optimized, thereby solving the technical problem of low efficiency in locating power system faults and achieving the technical effect of improving the efficiency of locating power system faults.

[0120] According to embodiments of the present invention, a power system fault location device is also provided. It should be noted that this power system fault location device can be used to execute the power system fault location method in the method embodiments.

[0121] Figure 3 is a schematic diagram of a power system fault location device according to an embodiment of the present invention. As shown in Figure 3, the power system fault location device 300 may include: an acquisition unit 301, a determination unit 302, and an analysis unit 303.

[0122] Acquisition unit 301 is used to acquire basic data of the power system, wherein the basic data is used to indicate the electrical parameters of multiple monitoring points in the power system.

[0123] The determining unit 302 is used to determine the graph structure information of the power system based on the basic data, wherein the graph structure information is used to represent the location of multiple monitoring points in the power system and the connection relationship between the multiple monitoring points.

[0124] Analysis unit 303 is used to input graph structure information into target detection model for analysis to obtain the fault location of the power system. The target detection model is trained using historical basic data of the power system, which is used to indicate the historical electrical parameters of multiple monitoring points in the power system.

[0125] Optionally, the determining unit 302 may include: a first determining module, used to determine each monitoring point of the power system based on basic data, wherein the monitoring point is used to indicate the location for monitoring the operating status of the power system; and a second determining module, used to determine the graph structure information of the power system based on the monitoring points.

[0126] Optionally, the second determining module may include: a determining submodule for determining monitoring point connection data based on the monitoring points, wherein the monitoring point connection data is used to indicate the connection information between the monitoring points; and a constructing submodule for constructing the monitoring points and monitoring point data into graph structure information of the power system.

[0127] Optionally, the power system fault location device 300 may further include: a first acquisition unit for acquiring historical basic data of the power system; and a training unit for training an initial detection model using the historical basic data to obtain a target detection model.

[0128] Optionally, the training unit may include: a third determining module for determining the historical fault types of power system faults corresponding to historical basic data; a fourth determining module for determining the adjustment parameters of the initial detection model based on the historical fault types and historical basic data; and a training module for training the initial detection model based on the adjustment parameters and historical basic data to obtain the target detection model.

[0129] Optionally, the power system fault location device 300 may further include: an evaluation unit for evaluating an initial detection model and determining the evaluation result of the initial detection model, wherein the evaluation result is used to indicate the accuracy of the initial detection model in locating power system faults; and a second determination unit for determining the initial detection model as a target detection model in response to the evaluation result being greater than an evaluation result threshold.

[0130] In this embodiment, basic data of the power system is acquired, wherein the basic data indicates the electrical parameters of multiple monitoring points in the power system; based on the basic data, the graph structure information of the power system is determined, wherein the graph structure information represents the location of multiple monitoring points in the power system and the connection relationships between multiple monitoring points; the graph structure information is input into a target detection model for analysis to obtain the fault location of the power system, wherein the target detection model is trained using historical basic data of the power system, and the historical basic data indicates the historical electrical parameters of multiple monitoring points in the power system. In other words, this embodiment of the invention establishes graph structure information of the power system through basic data of the power system, inputs the graph structure information into a target detection model for analysis, thereby determining the fault location of the power system, thus solving the technical problem of low efficiency in locating power system faults and achieving the technical effect of improving the efficiency of locating power system faults.

[0131] According to an embodiment of the present invention, a computer-readable storage medium is also provided, the storage medium including a stored program, wherein the program executes a method for locating power system faults in an embodiment of the method.

[0132] According to an embodiment of the present invention, a processor is also provided for running a program, wherein the program executes the power system fault location method in the method embodiment.

[0133] According to an embodiment of the present invention, a computer program product is also provided, the computer program product including computer instructions, which, when executed by a processor, implement the power system fault location method in the method embodiment.

[0134] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0135] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0137] The units described as separate components may or may not be physically separate. Similarly, the components shown as units may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0138] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0139] If the integrated unit is implemented as a software functional unit and sold or used as an independent functional component, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software functional component. This computer software functional component is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0140] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for locating faults in a power system, characterized in that, include: Acquire basic data of the power system, wherein the basic data is used to indicate the electrical parameters of multiple monitoring points in the power system; Based on the aforementioned basic data, the graph structure information of the power system is determined, wherein the graph structure information is used to represent the location of multiple monitoring points in the power system and the connection relationship between the multiple monitoring points; The graph structure information is input into the target detection model for analysis to obtain the fault location of the power system. The target detection model is trained using historical basic data of the power system, which is used to indicate the historical electrical parameters of multiple monitoring points in the power system.

2. The method according to claim 1, characterized in that, Based on the aforementioned basic data, the graph structure information of the power system is determined, including: Based on the aforementioned basic data, each of the monitoring points of the power system is determined, wherein the monitoring point is used to indicate the location for monitoring the operating status of the power system; Based on the monitoring points, the graph structure information of the power system is determined.

3. The method according to claim 2, characterized in that, Based on the monitoring points, the graph structure information of the power system is determined, including: Based on the monitoring points, the monitoring point connection data is determined, wherein the monitoring point connection data is used to indicate the connection information between the monitoring points; The monitoring points and their data are used to construct the graph structure information of the power system.

4. The method according to claim 1, characterized in that, The method further includes: Obtain the historical basic data of the power system; The initial detection model is trained using the historical data to obtain the target detection model.

5. The method according to claim 4, characterized in that, The historical baseline data is input into the initial detection model for training to obtain the target detection model, including: Determine the historical fault types of the power system corresponding to the historical basic data; Based on the historical fault types and the historical basic data, the adjustment parameters of the initial detection model are determined; Based on the adjusted parameters and the historical baseline data, the initial detection model is trained to obtain the target detection model.

6. The method according to claim 5, characterized in that, The method further includes: The initial detection model is evaluated to determine the evaluation result of the initial detection model, wherein the evaluation result is used to indicate the accuracy of the initial detection model in locating the power system fault; In response to the evaluation result being greater than the evaluation result threshold, the initial detection model is determined as the target detection model.

7. A fault location device for a power system, characterized in that, include: An acquisition unit is used to acquire basic data of a power system, wherein the basic data is used to indicate the electrical parameters of multiple monitoring points in the power system; A determining unit is configured to determine the graph structure information of the power system based on the basic data, wherein the graph structure information is used to represent the location of multiple monitoring points in the power system and the connection relationship between the multiple monitoring points; An analysis unit is used to input the graph structure information into a target detection model for analysis to obtain the fault location of the power system. The target detection model is trained using historical basic data of the power system, which is used to indicate the historical electrical parameters of multiple monitoring points in the power system.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program is run by a processor, it controls the device in which the storage medium is located to perform the method of any one of claims 1 to 6.

9. A processor, characterized in that, The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 6 when it runs.

10. A computer program product, characterized in that, The computer program product includes computer instructions that, when executed by a processor, implement the method described in any one of claims 1 to 6.

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