Method and apparatus for determining fault location in power system, and storage medium

Abstract

A method and apparatus for determining a fault location of a power system, and a storage medium. The method comprises: separately acquiring electrical data from a plurality of monitoring points of a power system (S101); on the basis of the electrical data, determining attribute data of the plurality of monitoring points in the power system (S102), the attribute data being used for indicating locations of the monitoring points and connection relationships among different monitoring points; and inputting the attribute data into a target detection model for analysis to obtain a fault location of the power system (S103), the target detection model being obtained by training using attribute data samples corresponding to electrical data samples of the power system, and the attribute data samples being used for indicating locations of monitoring point samples of the power system and connection relationships among different monitoring point samples. The present method solves the technical problem of low efficiency in determining a fault location in a power system.

Description

Methods, devices, and storage media for determining the location of faults in power systems

[0001] This application claims priority to Chinese Patent Application No. 202411544824.5, filed on October 31, 2024, entitled "Method, Apparatus and Storage Medium for Determining Fault Location in Power Systems", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This invention relates to the field of motion control technology for construction robots, and more specifically, to a method, apparatus, and storage medium for determining the location of a fault in a power system. 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, and storage medium for determining the location of a fault in a power system, thereby at least addressing the technical problem of low efficiency in determining the location of a fault in a power system.

[0007] According to one aspect of the present invention, a method for determining the location of a fault in a power system is provided. The method may include: acquiring electrical data from multiple monitoring points in the power system; determining attribute data for the multiple monitoring points based on the electrical data, wherein the attribute data indicates the location of the monitoring points and the connection relationships between different monitoring points; and inputting the attribute data into a target detection model for analysis to obtain the fault location of the power system, wherein the target detection model is trained using attribute data samples corresponding to electrical data samples of the power system, and the attribute data samples indicate the location of monitoring point samples in the power system and the connection relationships between different monitoring point samples.

[0008] Optionally, based on electrical data, attribute data of multiple monitoring points in the power system are determined, including: based on electrical data, determining characteristic data and distance data of the power system, wherein the characteristic data is used to indicate the operating status and / or performance information of the power system at the monitoring points, and the distance data is used to indicate the distance between multiple monitoring points; and based on the characteristic data and distance data, determining attribute data of multiple monitoring points in the power system.

[0009] Optionally, the method for determining the fault location of a power system includes: obtaining weight coefficients for monitoring points, wherein the weight coefficients are used to indicate the importance of the monitoring points; and adjusting characteristic data and / or distance data based on the weight coefficients.

[0010] Optionally, the attribute data is input into the target detection model for analysis to obtain the fault location of the power system, including: determining the pheromone concentration of multiple monitoring points based on the attribute data, wherein the pheromone concentration is used to indicate the probability of a fault occurring at the monitoring point; and inputting the pheromone concentration and attribute data into the target detection model for analysis to obtain the fault location of the power system.

[0011] Optionally, the method for determining the fault location of a power system may further include: training an initial detection model using attribute data samples of the power system to obtain a target detection model.

[0012] Optionally, the method for determining the location of a fault in a power system may further include: storing electrical data and / or the fault location.

[0013] According to another aspect of the present invention, an apparatus for determining the location of a fault in a power system is also provided. The apparatus may include: an acquisition unit for acquiring electrical data from multiple monitoring points in the power system; a determination unit for determining attribute data of the multiple monitoring points in the power system based on the electrical data, wherein the attribute data indicates the location of the monitoring points and the connection relationships between different monitoring points; and an analysis unit for inputting the attribute data into a target detection model for analysis to obtain the location of the fault in the power system, wherein the target detection model is trained using attribute data samples corresponding to the electrical data samples of the power system, and the attribute data samples indicate the location of the monitoring point samples of the power system and the connection relationships between different monitoring point samples.

[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 method for determining the fault location of a power system according to the embodiments of the present invention.

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

[0017] In this embodiment of the invention, electrical data from multiple monitoring points of a power system are acquired. Based on the electrical data, attribute data for the multiple monitoring points in the power system is determined. The attribute data indicates the location of the monitoring points and the connection relationships between different monitoring points. The attribute data is input into a target detection model for analysis to obtain the fault location of the power system. The target detection model is trained using attribute data samples corresponding to the electrical data samples of the power system. The attribute data samples indicate the location of the monitoring point samples in the power system and the connection relationships between different monitoring point samples. In other words, this invention uses electrical data from multiple monitoring points of the power system to determine the attribute data corresponding to each monitoring point, and then uses a target detection model to analyze the attribute data. This solves the technical problem of low efficiency in determining the fault location of a power system and achieves the technical effect of improving the efficiency of determining the fault location of a power system. 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 method for determining the fault location of a power system according to an embodiment of the present invention;

[0020] Figure 2 is a flowchart of a power fault location method based on an improved power system model combined with an ant colony algorithm according to an embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of a device for determining the fault location of a power system 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. 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 determining the location of a fault in a power system 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 method for determining the fault location of a power system according to an embodiment of the present invention. As shown in Figure 1, the method may include the following steps:

[0026] Step S101: Obtain electrical data from multiple monitoring points of the power system.

[0027] In the technical solution provided by step S101 of the present invention, electrical data can also be referred to as electrical parameters.

[0028] In this embodiment, electrical data from multiple monitoring points of the power system are acquired. This electrical data can include electrical parameters such as current, voltage, and power of the power system. For example, various sensors can be used to acquire the electrical data from multiple monitoring points of the power system. This is merely an example and does not limit the specific method for acquiring the electrical data from multiple monitoring points of the power system.

[0029] For example, sensors are installed at different monitoring points in the power system to acquire electrical data from multiple monitoring points.

[0030] Step S102: Based on electrical data, determine the attribute data of multiple monitoring points in the power system.

[0031] In the technical solution provided by step S102 of the present invention, attribute data is used to indicate the location of monitoring points and the connection relationship between different monitoring points.

[0032] In this embodiment, after obtaining the electrical data of multiple monitoring points in the power system in step S101, the attribute data of multiple monitoring points in the power system are determined based on the electrical data.

[0033] Optionally, characteristic data and distance data of the power system can be extracted from the electrical data, thereby determining the attribute data of multiple monitoring points in the power system based on the characteristic data and distance data. The characteristic data can also be referred to as electrical characteristics.

[0034] For example, a power system can be abstracted as a graph structure G = (V, E), where V is the set of nodes, representing the monitoring points in the power system; E is the set of edges, representing the connections between the monitoring points. For each node v in the graph structure information... i ∈V, assign the following attributes, that is, attribute data: 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. 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 )) is used to calculate the distance between nodes, that is, distance data.

[0035] Step S103: Input the attribute data into the target detection model for analysis to obtain the fault location of the power system.

[0036] In the technical solution provided in step S103 of the present invention, the target detection model is trained using attribute data samples corresponding to electrical data samples of the power system. The attribute data samples are used to indicate the location of monitoring point samples of the power system and the connection relationship between different monitoring point samples.

[0037] In this embodiment, after determining the attribute data of multiple monitoring points in the power system in step S102, the attribute data is input into the target detection model for analysis to obtain the fault location of the power system.

[0038] Optionally, historical data from the power system can be fed into an initial detection model for training to determine the target detection model. The initial detection model may include a fault location algorithm.

[0039] Optionally, the pheromone concentration at each monitoring point is determined based on attribute data, whereby the pheromone concentration indicates the probability of a fault occurring at the monitoring point. For example, an ant colony algorithm can be used to determine the pheromone concentration at each monitoring point; this is merely an example and does not limit the specific method for determining the pheromone concentration.

[0040] For example, consider monitoring points in a power system as ants, fault points as food sources, and connections between monitoring points as paths. Determine parameters such as the number of ants (N), initial pheromone value (T0), pheromone evaporation coefficient (ρ), and heuristic factors (α and β). Release ants to each monitoring point, and each ant selects the next monitoring point to move to based on the pheromone concentration at its current location and heuristic information from neighboring monitoring points. The probability of selecting the next monitoring point, i.e., the pheromone concentration at the next point, can be calculated using the following formula (1):

[0041] in, Let t represent the probability that the k-th ant chooses to move from monitoring point i to monitoring point j. ij η represents the pheromone concentration along the path from monitoring point i to monitoring point j. ij This represents the heuristic information from monitoring point i to monitoring point j, where α and β are adjustment parameters, and s∈allowed. k Let η represent the set of monitoring points that the k-th ant can choose. Heuristic information can be calculated based on factors such as differences in electrical parameters and distance. Heuristic information can be defined by the following formula (2): η ij=1 / d ij (2)

[0042] Where, d ij This represents the distance from monitoring point i to monitoring point j.

[0043] Optionally, after determining the pheromone concentration at each monitoring point, the fault location of the power system can be determined based on the pheromone concentration and the target detection model.

[0044] It should be noted that the above embodiments can be executed using a power system fault location determination device.

[0045] In steps S101 to S103 of this invention, electrical data from multiple monitoring points of a power system are acquired. Based on the electrical data, attribute data for the multiple monitoring points in the power system is determined. The attribute data indicates the location of the monitoring points and the connection relationships between different monitoring points. The attribute data is then input into a target detection model for analysis to obtain the fault location of the power system. The target detection model is trained using attribute data samples corresponding to the electrical data samples of the power system. The attribute data samples indicate the location of the monitoring point samples in the power system and the connection relationships between different monitoring point samples. In other words, this invention uses electrical data from multiple monitoring points of the power system to determine the attribute data corresponding to each monitoring point, and then uses a target detection model to analyze the attribute data. This solves the technical problem of low efficiency in determining the fault location of a power system and achieves the technical effect of improving the efficiency of determining the fault location of a power system.

[0046] The method described in this embodiment will be further described below.

[0047] As an optional embodiment, the attribute data of multiple monitoring points in the power system is determined based on electrical data, including: determining characteristic data and distance data of the power system based on electrical data, wherein the characteristic data is used to indicate the operating status and / or performance information of the power system at the monitoring points, and the distance data is used to indicate the distance between multiple monitoring points; and determining the attribute data of multiple monitoring points in the power system based on the characteristic data and distance data.

[0048] In this embodiment, characteristic data and / or distance data of the power system are determined based on electrical data. The specific determination method is as shown in the aforementioned formula (1), and will not be repeated here.

[0049] Optionally, by determining the characteristic data and distance data of the power system, faults can be located based on the specific characteristics of the power system, thereby improving the efficiency of determining the fault location of the power system.

[0050] As an optional embodiment, the method for determining the fault location of a power system includes: obtaining weight coefficients for monitoring points, wherein the weight coefficients are used to indicate the importance of the monitoring points; and adjusting characteristic data and / or distance data based on the weight coefficients.

[0051] In this embodiment, the weight coefficients of the monitoring points are obtained, wherein the weight coefficients can be w. ij To express.

[0052] For example, weight w ij It can be expressed by the following formula (3): E ij =k1×|I i -I j |+k2×|V i -V j |+k3×|W i -W J |+…; (3)

[0053] Where K1, K2, K3, ... can represent adjustment coefficients, which can be adjusted according to the actual situation.

[0054] Optionally, after determining the weighting coefficients, the characteristic data and distance data can be adjusted using the adjustment coefficients in the weighting coefficients.

[0055] As an optional implementation method, the attribute data is input into the target detection model for analysis to obtain the fault location of the power system, including: determining the pheromone concentration of multiple monitoring points based on the attribute data, wherein the pheromone concentration is used to indicate the probability of a fault occurring at the monitoring point; and inputting the pheromone concentration and attribute data into the target detection model for analysis to obtain the fault location of the power system.

[0056] In this embodiment, pheromone concentrations at multiple monitoring points are determined based on attribute data. The pheromone concentrations and attribute data are then input into a target detection model for analysis to determine the location of the power system fault. For example, the pheromone concentrations are updated using an ant colony algorithm included in the target detection model to determine the fault location in the power system.

[0057] For example, when some ants find a possible fault point or after a certain number of iterations, the pheromone is updated. The pheromone concentration is increased along the path taken by the ants that found the actual fault point. The pheromone concentration update formula is as follows (4): T ij =(1-ρ)T ij +ΔT ij (4)

[0058] Where ρ is the pheromone evaporation coefficient. This represents the increment of pheromone left by the k-th ant on the path from monitoring point i to monitoring point j. It can be expressed as the following formula (5):

[0059] If the k-th ant traverses the path and finds the fault point, then Where Q is a constant, L k Let T represent the path length traversed by the k-th ant. For paths traversed by ants that did not find the fault point, the pheromone concentration decreases by T according to the evaporation coefficient. ij =(1-ρ)T ij Gradually reduce information that is unlikely to lead to the failure point to avoid ants wasting time on these paths.

[0060] As an optional embodiment, the method for determining the fault location of a power system further includes: training an initial detection model using attribute data samples of the power system to obtain a target detection model.

[0061] In this embodiment, attribute data samples of the power system are input into an initial detection model for training to obtain a target detection model. The initial detection model can also be an ant colony algorithm.

[0062] Optionally, by training the initial detection model, a target detection model can be obtained, which can determine the accuracy of the target detection model in detecting the fault location of the power system, thereby improving the efficiency of determining the fault location of the power system.

[0063] As an optional embodiment, the method for determining the fault location of a power system further includes: storing electrical data and / or the fault location.

[0064] In this embodiment, electrical data and / or fault locations are stored. For example, the electrical data and fault locations are uploaded to a server for storage. This is merely an example and does not limit the specific method of storing electrical data and / or fault locations.

[0065] Optionally, by storing electrical data and / or fault locations, it is possible to ensure that the stored records can be retrieved during subsequent fault detection processes, thereby achieving the purpose of one-step fault location and improving the efficiency of determining the fault location of the power system.

[0066] It should be noted that the above embodiments can be executed using a power system fault location determination device.

[0067] In this embodiment, electrical data from multiple monitoring points of the power system are acquired. Based on the electrical data, attribute data for the multiple monitoring points in the power system is determined. The attribute data indicates the location of the monitoring points and the connection relationships between different monitoring points. The attribute data is input into a target detection model for analysis to obtain the fault location of the power system. The target detection model is trained using attribute data samples corresponding to the electrical data samples of the power system. The attribute data samples indicate the location of the monitoring point samples in the power system and the connection relationships between different monitoring point samples. In other words, this invention uses electrical data from multiple monitoring points of the power system to determine the attribute data corresponding to each monitoring point, and then uses a target detection model to analyze the attribute data. This solves the technical problem of low efficiency in determining the fault location of the power system and achieves the technical effect of improving the efficiency of determining the fault location of the power system.

[0068] The technical solutions of the embodiments of the present invention will be illustrated below with reference to preferred embodiments.

[0069] 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.

[0070] 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.

[0071] However, this invention proposes a power fault location method based on an improved power system model combined with an ant colony algorithm. By collecting electrical parameters such as current, voltage, and power, the attributes of nodes and edges in the power system model are determined based on the collected data. The ant colony algorithm is then activated to locate the fault. This solves the technical problem of low efficiency in determining the location of power system faults and achieves the technical effect of improving the efficiency of fault location determination.

[0072] The embodiments of the present invention will be further described below.

[0073] Figure 2 is a flowchart of a power fault location method based on an improved power system model combined with an ant colony algorithm according to an embodiment of the present invention. The analysis method includes the following steps:

[0074] Step S201: Obtain the electrical parameters of the power system.

[0075] 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.

[0076] Step S202: Based on the collected data, construct the electrical characteristic matrix and distance matrix to determine the attributes of nodes and edges in the power system model.

[0077] 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.

[0078] 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.

[0079] 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 (3).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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 (6):

[0085] 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.

[0086] 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.

[0087] 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.

[0088] Alternatively, the weighting function can be expressed using the following formula (7): w ij =f(M E (i,j),M D (i,j)); (7)

[0089] Optionally, the model can be further refined using a linear function, which can be expressed using the following formula (8): f(M E (i,j),M D (i,j))=(M E (i,j) γ +1)×(M D (i,j) δ +1); (8)

[0090] Here, γ and δ are adjustment parameters used to balance the contributions of electrical characteristics and distance to edge weights.

[0091] Step S203: Start the ant colony algorithm to locate the fault.

[0092] In this embodiment, an ant colony algorithm is initiated for fault location. The performance of fault location is optimized by adjusting the algorithm parameters and adjustment coefficients in the model.

[0093] Optionally, monitoring points in the power system can be considered as ants, fault points as food sources, and connections between monitoring points as paths. Parameters such as the number of ants (N), initial pheromone value (T0), pheromone evaporation coefficient (ρ), and heuristic factors (α and β) are determined. Ants are released to each monitoring point, and each ant selects its next moving monitoring point based on the pheromone concentration at its current location and heuristic information from neighboring monitoring points. The probability of selecting the next monitoring point, i.e., the pheromone concentration at the next point, can be calculated using the aforementioned formula (1), which will not be elaborated here.

[0094] Optionally, Let t represent the probability that the k-th ant chooses to move from monitoring point i to monitoring point j. ij η represents the pheromone concentration along the path from monitoring point i to monitoring point j. ij This represents the heuristic information from monitoring point i to monitoring point j, where α and β are adjustment parameters, and s∈allowed. kLet represent the set of monitoring points that the k-th ant can choose. Heuristic information can be calculated based on factors such as differences in electrical parameters and distance. Heuristic information can be defined as the aforementioned formula (2), which will not be elaborated here.

[0095] Optionally, pheromone updates are performed after some ants have found a possible fault point or after a certain number of iterations. The pheromone concentration is increased along the path taken by the ants that found the actual fault point. The pheromone concentration update formula is the aforementioned formula (4), which will not be repeated here.

[0096] Optionally, This represents the increment of pheromone left by the k-th ant on the path from monitoring point i to monitoring point j. It can be expressed as the aforementioned formula (5), which will not be elaborated here.

[0097] Optionally, if the k-th ant traverses the path and finds the fault point, then Where Q is a constant, L k Let T represent the path length traversed by the k-th ant. For paths traversed by ants that did not find the fault point, the pheromone concentration decreases by T according to the evaporation coefficient. ij =(1-ρ)T ij Gradually reduce information that is unlikely to lead to the failure point to avoid ants wasting time on these paths.

[0098] Step S204: Determine the location of the fault based on the fault location.

[0099] In this embodiment, after multiple iterations, the pheromone concentration gradually concentrates on the path more likely to lead to the fault point. The most likely fault location is determined based on the path with the highest pheromone concentration.

[0100] 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 and online fault location methods.

[0101] Optionally, offline fault location methods include: pulse signal injection method and fault indicator method. The pulse signal injection method injects a high-voltage pulse signal into the disconnected faulty line and then detects the fault point along the line. Specifically, DC signals are used to determine the fault segment, and AC signals are used to determine the fault point. The pulse signal injection method requires power outage and disconnection of the faulty line, while also requiring onboard power, which may cause some inconvenience in practical operation.

[0102] 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 flips its label, illuminates, or emits an alarm signal. Although 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 and partial discharge. 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. The S-signal injection method requires a significant investment and may increase line maintenance workload, while also posing certain safety hazards.

[0104] 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. The partial discharge method requires high precision from the monitoring equipment and may be affected by environmental factors.

[0105] Alternatively, the cable reflection method locates faults 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 fault distance is calculated based on the signal transmission speed and reflection time. The accuracy of the cable reflection method may be affected by factors such as cable length, load resistance, and fault type.

[0106] Optionally, the time-domain reflectometry (TD-RESO) method analyzes 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. However, for signals propagating over long distances, the TD-RESO method is less accurate due to increased factors affecting wave speed, leading to increased location errors.

[0107] Alternatively, infrared thermography 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. However, infrared thermography is affected by ambient temperature and cable heat dissipation conditions, and may not accurately reflect the actual temperature at the fault point.

[0108] In this embodiment, electrical parameters such as current, voltage, and power are collected, and the attributes of nodes and edges in the power system model are determined based on the collected data. An ant colony algorithm is then initiated to locate the fault; thus solving the technical problem of low efficiency in determining the fault location of a power system and achieving the technical effect of improving the efficiency of fault location determination.

[0109] According to an embodiment of the present invention, a device for determining the fault location of a power system is also provided. It should be noted that this device for determining the fault location of a power system can be used to execute the method for determining the fault location of a power system in Embodiment 1.

[0110] Figure 3 is a schematic diagram of a fault location determination device for a power system according to an embodiment of the present invention. As shown in Figure 3, the fault location determination device 300 for the power system may include: an acquisition unit 301, a determination unit 302, and an analysis unit 303.

[0111] The acquisition unit 301 is used to acquire electrical data from multiple monitoring points of the power system.

[0112] The determining unit 302 is used to determine the attribute data of multiple monitoring points in the power system based on electrical data, wherein the attribute data is used to indicate the location of the monitoring points and the connection relationship between different monitoring points.

[0113] The analysis unit 303 is used to input attribute data into the target detection model for analysis to obtain the fault location of the power system. The target detection model is trained using attribute data samples corresponding to electrical data samples of the power system. The attribute data samples are used to indicate the location of monitoring point samples of the power system and the connection relationship between different monitoring point samples.

[0114] Optionally, the determining unit 302 may include: a first determining module, used to determine characteristic data and distance data of the power system based on electrical data, wherein the characteristic data is used to indicate the operating status and / or performance information of the power system at the monitoring points, and the distance data is used to indicate the distance between multiple monitoring points; and a second determining module, used to determine attribute data of multiple monitoring points in the power system based on the characteristic data and distance data.

[0115] Optionally, the fault location determination device 300 of the power system may further include: a first acquisition unit for acquiring weight coefficients of monitoring points, wherein the weight coefficients are used to indicate the importance of the monitoring points; and an adjustment unit for adjusting characteristic data and / or distance data based on the weight coefficients.

[0116] Optionally, the analysis unit 303 may include: a third determining module, used to determine the pheromone concentration of multiple monitoring points based on attribute data, wherein the pheromone concentration is used to indicate the probability of a fault occurring at the monitoring point; and an analysis module, used to input the pheromone concentration and attribute data into the target detection model for analysis to obtain the fault location of the power system.

[0117] Optionally, the fault location determination device 300 of the power system may further include: a training unit for training an initial detection model using attribute data samples of the power system to obtain a target detection model.

[0118] Optionally, the fault location determination device 300 of the power system may further include: a storage unit for storing electrical data and / or fault location.

[0119] In this embodiment, electrical data from multiple monitoring points of the power system are acquired. Based on the electrical data, attribute data for the multiple monitoring points in the power system is determined. The attribute data indicates the location of the monitoring points and the connection relationships between different monitoring points. The attribute data is input into a target detection model for analysis to obtain the fault location of the power system. The target detection model is trained using attribute data samples corresponding to the electrical data samples of the power system. The attribute data samples indicate the location of the monitoring point samples in the power system and the connection relationships between different monitoring point samples. In other words, this invention uses electrical data from multiple monitoring points of the power system to determine the attribute data corresponding to each monitoring point, and then uses a target detection model to analyze the attribute data. This solves the technical problem of low efficiency in determining the fault location of the power system and achieves the technical effect of improving the efficiency of determining the fault location of the power system.

[0120] 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 the method for determining the fault location of a power system in Embodiment 1.

[0121] According to an embodiment of the present invention, a processor is also provided for running a program, wherein the program executes the method for determining the fault location of the power system in Embodiment 1.

[0122] According to an embodiment of the present invention, a computer program product is also provided, which includes computer instructions that, when executed by a processor, implement the method for determining the fault location of the power system in Embodiment 1.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, 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 according to actual needs.

[0127] 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.

[0128] 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.

[0129] 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 determining the location of a fault in a power system, characterized in that, include: Acquire electrical data from multiple monitoring points in the power system; Based on the electrical data, attribute data of multiple monitoring points in the power system are determined, wherein the attribute data is used to indicate the location of the monitoring points and the connection relationship between different monitoring points; The attribute data 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 attribute data samples corresponding to the electrical data samples of the power system. The attribute data samples are used to indicate the location of the monitoring point samples of the power system and the connection relationship between different monitoring point samples.

2. The method according to claim 1, characterized in that, Based on the electrical data, attribute data of multiple monitoring points in the power system are determined, including: Based on the electrical data, characteristic data and distance data of the power system are determined, wherein the characteristic data is used to indicate the operating status and / or performance information of the power system at the monitoring point, and the distance data is used to indicate the distance between multiple monitoring points; Based on the characteristic data and the distance data, the attribute data of multiple monitoring points in the power system are determined.

3. The method according to claim 2, characterized in that, The method further includes: Obtain the weight coefficient of the monitoring point, wherein the weight coefficient is used to indicate the importance of the monitoring point; Based on the weighting coefficients, adjust the characteristic data and / or the distance data.

4. The method according to claim 1, characterized in that, The attribute data is input into the target detection model for analysis to obtain the fault location of the power system, including: Based on the attribute data, the pheromone concentrations at multiple monitoring points are determined, wherein the pheromone concentrations are used to indicate the probability of a fault occurring at the monitoring point. The pheromone concentration and the attribute data are input into the target detection model for analysis to obtain the fault location of the power system.

5. The method according to claim 1, characterized in that, The method further includes: The initial detection model is trained using the attribute data samples of the power system to obtain the target detection model.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Store the electrical data and / or the fault location.

7. A device for determining the location of a fault in a power system, characterized in that, include: The acquisition unit is used to acquire electrical data from multiple monitoring points in the power system. A determining unit is configured to determine attribute data of multiple monitoring points in the power system based on the electrical data, wherein the attribute data is used to indicate the location of the monitoring points and the connection relationship between different monitoring points; An analysis unit is used to input the attribute data into a target detection model for analysis to obtain the fault location of the power system. The target detection model is trained using attribute data samples corresponding to electrical data samples of the power system. The attribute data samples are used to indicate the location of monitoring point samples of the power system and the connection relationship between different monitoring point samples.

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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