Line loss anomaly detection method and device for power system and computer equipment

Abstract

The invention discloses a line loss anomaly detection method and device for a power system and computer equipment. The method comprises the following steps: acquiring an abnormal node in a power system and abnormal operation data corresponding to the abnormal node; based on the abnormal node, multiple target nodes are determined, and the target nodes are nodes associated with the abnormal node; based on the abnormal operation data, to-be-calculated operation data are estimated, and the to-be-calculated operation data are operation data generated under the normal condition; based on the to-be-calculated operation data, the to-be-calculated line loss rate of the abnormal node and the to-be-calculated line loss rate of each target node are estimated, and the to-be-calculated line loss rate is the corresponding line loss rate under the normal condition; and based on the planned line loss rate, determining an anomaly detection result of the power system, the anomaly detection result including line loss anomaly factors of the power system. According to the invention, the technical problem that a traditional manual troubleshooting method is not accurate and rapid in positioning of an abnormal reason is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic information, in particular to a line loss anomaly detection method and device for a power system and a computer device. BACKGROUND

[0002] In the operation of the power industry, the integrated line loss rate as a key parameter reflecting the efficiency of the power system, its management and control is particularly important. However, the traditional line loss analysis method gradually shows its limitations when facing the increasingly complex and huge power grid structure. Specifically, when the integrated line loss anomaly occurs in the power system, that is, the line loss rate exceeds the reasonable threshold or unexpected fluctuations occur, the existing method often fails to quickly and accurately diagnose the abnormal causes and the influence range of the anomaly on each part of the power system.

[0003] The traditional manual diagnosis process relies on the experience and intuition of line loss management personnel, and analyzes line loss anomalies through comparison of historical data, on-site inspection and expert judgment. This method may be effective in dealing with simple problems, but it is not up to the task when faced with complex anomalies caused by multiple factors. For example, when the power data of a certain power supply port is abnormal, it needs to analyze its influence on the line loss of the partition, voltage and element, and whether there is electricity stealing behavior. Manual analysis is difficult to systematically investigate and determine the true source of the anomaly, especially it cannot intuitively show the influence range of the abnormal point, resulting in a long diagnosis process and easy to miss important information.

[0004] At present, there is no effective solution to the above problems. SUMMARY

[0005] The embodiments of the present application provide a line loss anomaly detection method, device and computer device for a power system, to at least solve the technical problem that the traditional manual investigation method is not accurate and fast enough in locating the abnormal causes.

[0006] According to an aspect of the embodiments of the present application, a line loss anomaly detection method for a power system is provided, comprising: acquiring an abnormal node in the power system and abnormal operation data corresponding to the abnormal node, wherein the abnormal node is a node with abnormal line loss rate; determining a plurality of target nodes based on the abnormal node, wherein the target node is a node associated with the abnormal node; estimating the quasi-calculated operation data based on the abnormal operation data, wherein the quasi-calculated operation data is the operation data generated under normal circumstances; estimating the quasi-calculated line loss rate of the abnormal node and the quasi-calculated line loss rate of each of the plurality of target nodes based on the quasi-calculated operation data, wherein the quasi-calculated line loss rate is the corresponding line loss rate under normal circumstances; determining the abnormal detection result of the power system based on the quasi-calculated line loss rate, wherein the abnormal detection result includes the line loss anomaly factors of the power system.

[0007] Optionally, the acquiring the abnormal node in the power system comprises: acquiring a comprehensive line loss rate of the power system, wherein the comprehensive line loss rate is determined based on power supply port power, distributed power supply power, high-voltage user power and low-voltage user power; determining whether the comprehensive line loss rate exceeds a first preset threshold, wherein the first preset threshold is determined based on the comprehensive line loss rate of the power system in a preset historical time; and determining the abnormal node in a case where the comprehensive line loss rate exceeds the first preset threshold.

[0008] Optionally, the acquiring the abnormal operation data corresponding to the abnormal node comprises: acquiring operation data corresponding to each of a plurality of dimensions of the abnormal node, wherein the plurality of dimensions comprise an archive information dimension, a model topology dimension, a metering data dimension and a electricity stealing data dimension; and respectively determining whether the operation data corresponding to each of the plurality of dimensions is abnormal, and taking the operation data that is abnormal as the abnormal operation data.

[0009] Optionally, the determining a plurality of target nodes based on the abnormal node comprises: taking the abnormal node as a first node to construct an initial queue; taking the first node from the initial queue to determine whether the first node is a target node; in a case where the first node is not a target node, adding all other nodes adjacent to the first node to the initial queue; and repeating the above process until there is no node in the initial queue, and determining the plurality of target nodes.

[0010] Optionally, the estimating the estimated operation data based on the abnormal operation data comprises: determining a plurality of historical operation data based on the abnormal operation data, wherein the plurality of historical operation data are operation data corresponding to a plurality of dates before the day when the abnormal operation data is generated, and the interval between the plurality of dates is one week; and determining an average value of the plurality of historical operation data as the estimated operation data.

[0011] Optionally, the determining the abnormal detection result of the power system based on the estimated line loss rate comprises: respectively calculating a difference between the estimated line loss rate of each of the abnormal node and the plurality of target nodes and a corresponding standard line loss rate to obtain a plurality of initial differences; comparing the sizes of the plurality of initial differences to determine that an initial difference exceeding a second preset threshold in the plurality of initial differences is a target difference; and determining the abnormal detection result based on the target difference.

[0012] According to another aspect of the embodiments of the present application, there is also provided a line loss anomaly detection apparatus for a power system, comprising: an acquisition module configured to acquire an anomaly node in the power system and anomaly operation data corresponding to the anomaly node, wherein the anomaly node is a node with an abnormal line loss rate; a first determination module configured to determine a plurality of target nodes based on the anomaly node, wherein the target node is a node associated with the anomaly node; a first estimation module configured to estimate pseudo-calculated operation data based on the anomaly operation data, wherein the pseudo-calculated operation data is operation data generated under normal conditions; a second estimation module configured to estimate a pseudo-calculated line loss rate of the anomaly node and a pseudo-calculated line loss rate of each of the plurality of target nodes based on the pseudo-calculated operation data, wherein the pseudo-calculated line loss rate is a corresponding line loss rate under normal conditions; and a second determination module configured to determine an anomaly detection result of the power system based on the pseudo-calculated line loss rates, wherein the anomaly detection result comprises a line loss anomaly factor of the power system.

[0013] According to still another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium comprising a stored program, wherein the program, when executed, controls a device in which the non-volatile storage medium is located to perform any one of the line loss anomaly detection methods for a power system described above.

[0014] According to yet another aspect of the embodiments of the present application, there is also provided a computer device comprising a processor configured to execute a program, wherein the program, when executed, performs any one of the line loss anomaly detection methods for a power system described above.

[0015] According to still another aspect of the embodiments of the present application, there is also provided a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements any one of the line loss anomaly detection methods for a power system described above.

[0016] In the embodiment of the present application, the line loss anomaly detection method for the power system is adopted, the abnormal node in the power system and the abnormal operation data corresponding to the abnormal node are obtained, wherein the abnormal node is a node with abnormal line loss rate; based on the abnormal node, a plurality of target nodes are determined, wherein the target node is a node associated with the abnormal node; based on the abnormal operation data, the estimated operation data is estimated, wherein the estimated operation data is the operation data generated under normal circumstances; based on the estimated operation data, the estimated line loss rate of the abnormal node and the estimated line loss rate of each of the plurality of target nodes are respectively estimated, wherein the estimated line loss rate is the corresponding line loss rate under normal circumstances; based on the estimated line loss rate, the anomaly detection result of the power system is determined, wherein the anomaly detection result includes the line loss anomaly factor of the power system, which achieves the purpose of accurately judging the line loss anomaly reason of the power system, thereby realizing the technical effect of improving the efficiency and safety of the power system operation, and further solving the technical problem that the traditional manual troubleshooting method is not accurate and fast enough in positioning the abnormal reason. BRIEF DESCRIPTION OF DRAWINGS

[0017] The drawings described herein are used to provide further understanding of the present application, constitute a part of the present application, the schematic embodiments of the present application and the description thereof are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 A hardware structure block diagram of a computer terminal for implementing the line loss anomaly detection method for the power system is shown;

[0019] Figure 2 is a flowchart of the line loss anomaly detection method for the power system provided according to the embodiment of the present application;

[0020] Figure 3 is a flowchart of the power supply port file anomaly diagnosis provided according to the optional embodiment of the present application;

[0021] Figure 4 is a flowchart of the power supply port model anomaly diagnosis provided according to the optional embodiment of the present application;

[0022] Figure 5 is a flowchart of the power supply port metering anomaly diagnosis provided according to the optional embodiment of the present application;

[0023] Figure 6 is a flowchart of the comprehensive line loss anomaly diagnosis method based on the breadth-first search provided according to the optional embodiment of the present application;

[0024] Figure 7 is a flowchart of the power supply port electric quantity anomaly diagnosis method provided according to the optional embodiment of the present application;

[0025] Figure 8 is a structural block diagram of a line loss anomaly detection device for a power system according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the persons skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by the persons skilled in the art without creative labor should belong to the protection scope of the present application.

[0027] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be exchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] According to an embodiment of the present application, an embodiment of a line loss anomaly detection method for a power system is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0029] The method embodiment provided by the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 A hardware structural block diagram of a computer terminal for implementing a line loss anomaly detection method for a power system is shown. As shown in the figure, Figure 1As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0030] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be implemented wholly or partially as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be wholly or partially integrated into any other element in the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0031] The memory 104 can be used to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the line loss anomaly detection method for power systems in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the aforementioned application program for the line loss anomaly detection method for power systems. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0032] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0033] Figure 2 This is a flowchart illustrating a method for detecting abnormal line losses in a power system according to an embodiment of the present invention, as shown below. Figure 2As shown, the method comprises the following steps:

[0034] In step S201, an abnormal node in the power system and abnormal operation data corresponding to the abnormal node are obtained, wherein the abnormal node is a node with abnormal line loss rate.

[0035] In this step, when the power system is running, the operation data of each node (such as a power supply port, a substation, a bus, etc.) will be continuously monitored. For example, from the perspective of file information, the completeness and accuracy of the equipment file can be checked, including the type of equipment, the voltage level, the connection mode, etc.; from the perspective of model topology, an accurate power system model is constructed using the topology of the power network to reflect the electrical connection and energy flow relationship between devices in the power grid; from the perspective of metering data, real-time monitoring of the metering information of the electric energy meter is performed, including the power supply capacity of the power supply port, the power generation capacity of the distributed power supply, the power consumption of high-voltage users and low-voltage users, and the meter bottom data of the electric energy meter; from the perspective of electricity stealing data, the data collected by the anti-electricity stealing system is analyzed to identify possible electricity stealing behavior.

[0036] Once it is found that the above operation data of a certain node is abnormal, it is considered to be an abnormal operation state, and the node is marked as an abnormal node. Then, further analysis is performed on these abnormal nodes to identify the specific operation data that causes the abnormal line loss rate. This may include missing or incorrect meter bottom data, abnormal fluctuations in port power, incomplete or false equipment file information, inaccurate model topology structure, etc. By deeply diagnosing the operation data of the abnormal node, the specific source of the problem can be located, providing direct data support and analysis basis for subsequent power calculation, line loss abnormality investigation and governance.

[0037] In step S202, based on the abnormal node, a plurality of target nodes are determined, wherein the target node is a node associated with the abnormal node.

[0038] In this step, starting from the abnormal node, a traversal search can be performed on the power network to identify all target nodes related to it. The target node refers to those nodes that are directly or indirectly connected to the abnormal node in the topology of the power system, which may be affected by the abnormal node due to electrical connection, data sharing or system synergy effect. For example, a breadth-first search algorithm can be used for analysis, which starts from the abnormal node and expands the search layer by layer outward. For example, taking the power supply port as the starting node, this algorithm can quickly and comprehensively search for element ports connected to it, such as voltage division ports, substations, buses, transmission lines, etc., and then continue to expand the search outward from these port nodes to determine the affected units, voltage level division line loss, substation loss, etc.

[0039] In step S203, based on the abnormal operation data, estimated calculation operation data is estimated, wherein the estimated calculation operation data is operation data generated under normal circumstances.

[0040] In this step, based on the abnormal operation data, the process of estimating the pseudo-calculated operation data is mainly to identify and correct the abnormal line loss phenomena in the power system caused by various reasons (such as equipment failure, metering error, data missing, etc.). The pseudo-calculated operation data refers to the operation data under the assumption that the power system is in an ideal normal state. The estimation of such data is crucial for restoring the normal operation of the system and keeping the line loss rate within a reasonable range. For abnormal operation data that directly affects line loss calculation, such as missing or abnormal table bottom data, common pseudo-calculation algorithms can be applied, such as the average value method and the periodic average method. The average value method generally uses the average operation data of the abnormal node in the previous period as the pseudo-calculated value, which is suitable for relatively stable data trends. The periodic average method considers the change of electricity in special periods such as weekends and holidays, and selects the average value in the same period before and after the abnormal data to eliminate the periodic influence. For abnormal operation data that indirectly affects the comprehensive line loss calculation, such as missing basic data quality problems such as the rate of the gateway metering point and the effective time of the electric energy meter, do not participate in the electricity pseudo-calculation, but through the management of abnormal data in the file and model, improve the quality of the basic data of the comprehensive line loss calculation.

[0041] Step S204, based on the pseudo-calculated operation data, respectively estimating the pseudo-calculated line loss rate of the abnormal node and the pseudo-calculated line loss rate of each of the plurality of target nodes, wherein the pseudo-calculated line loss rate is the line loss rate corresponding to the normal situation.

[0042] In this step, for each node (including the abnormal node and the plurality of target nodes), a line loss calculation model reflecting its electrical characteristics and operating conditions needs to be established. These models are based on the basic physical formulas and device parameters of the power system and can predict the line loss rate of the node under given operating data. Once the model is established, the pseudo-calculated operation data can be used to estimate the line loss rate. The pseudo-calculated operation data is used to replace the abnormal node in the line loss calculation model, and the line loss rate is recalculated. The line loss rate at this time is the pseudo-calculated line loss rate, which is the line loss performance of the abnormal node under normal operating conditions after excluding the interference of abnormal factors. For the plurality of target nodes that have direct or indirect electrical connection with the abnormal node, the pseudo-calculated operation data obtained above can be used to estimate the line loss rate. The pseudo-calculated operation data of the abnormal node is applied to the line loss calculation model of the target node with direct or indirect electrical connection as a new input parameter.

[0043] Step S205, based on the pseudo-calculated line loss rate, determining the abnormal detection result of the power system, wherein the abnormal detection result includes the line loss abnormal factor of the power system.

[0044] In this step, by the pseudo-calculated line loss rate of each node, the cause of the line loss anomaly can be inferred. For example, the actual line loss rate of each node can be compared with the pseudo-calculated line loss rate. For nodes with a large difference between the actual line loss rate and the pseudo-calculated line loss rate, further analysis can be performed to find the underlying abnormal reasons. Abnormal factors can include metering equipment failure, data transmission error, electricity stealing behavior, power grid structure problem, weather influence, etc. The diagnosis process can involve data analysis, on-site inspection, and cross-validation with other system information. After comparison and detailed diagnosis, the power system anomaly detection results including abnormal factors are output. These results not only indicate which part of the system is abnormal, but also provide specific information about the cause of the anomaly, providing guidance for subsequent fault handling and preventive measures.

[0045] Through the above steps, the purpose of accurately determining the line loss anomaly reason of the power system is achieved, thereby realizing the technical effect of improving the efficiency and safety of the power system operation, and further solving the technical problem that the traditional manual troubleshooting method is not accurate and fast enough in locating the abnormal reason.

[0046] As an optional embodiment, the abnormal node in the power system is obtained, including: obtaining a comprehensive line loss rate of the power system, wherein the comprehensive line loss rate is determined based on power supply port power, distributed power supply power, high-voltage user power, and low-voltage user power; determining whether the comprehensive line loss rate exceeds a first preset threshold, wherein the first preset threshold is determined based on the comprehensive line loss rate of the power system in a preset historical time; and in the case that the comprehensive line loss rate exceeds the first preset threshold, determining the abnormal node.

[0047] Optionally, the first preset threshold can be set reasonably in combination with the historical data of the comprehensive line loss rate, and when the line loss rate exceeds the first preset threshold, the line loss anomaly troubleshooting process is started to determine the abnormal node. For example, the T-day comprehensive line loss rate can be obtained, the mean and standard deviation of the historical data are calculated, and the first preset threshold is set as the mean ± [X] times the standard deviation, wherein [X] is determined according to the actual situation and experience. If the comprehensive line loss rate exceeds the first preset threshold, the line loss anomaly troubleshooting process is started to determine the abnormal node. The comprehensive line loss rate is calculated from the power supply port power, the distributed power supply power, the high-voltage user power, and the substation sales (low-voltage user power).

[0048] As an optional embodiment, the abnormal operation data corresponding to the abnormal node is obtained, including: obtaining operation data corresponding to each of a plurality of dimensions of the abnormal node, wherein the plurality of dimensions include an archive information dimension, a model topology dimension, a metering data dimension, and a electricity stealing data dimension; respectively determining whether the operation data corresponding to each of the plurality of dimensions is abnormal, and taking the operation data with the abnormality as the abnormal operation data.

[0049] Optionally, the acquisition of abnormal operation data can be performed from multiple dimensions such as archives, models, metering, electricity stealing, etc. Through constructing a line loss diagnosis and analysis model, the power supply port, distributed power supply, high-voltage user electricity sales, and transformer area electricity sales (low-voltage user electricity) under the comprehensive line loss are diagnosed and analyzed to check whether there is abnormal data in the regional line loss. For example, taking the power supply port as an example, according to the algorithm rule model, it can be subdivided into three categories: archive abnormal diagnosis model, model topology abnormal diagnosis model, and metering abnormal diagnosis model.

[0050] Figure 3 is a flowchart of power supply port archive abnormal diagnosis provided according to an optional embodiment of the present application, as shown in Figure 3 , first, the comprehensive line loss power supply port archive information can be input, and combined with the archive abnormal diagnosis model, it is judged whether there is a case that the power supply port metering point ratio is empty, the power supply port metering table effective time is empty, the table replacement record is unreasonable or missing, etc. If the power supply port archive is abnormal, the power supply port archive abnormal details are output. Figure 4 is a flowchart of power supply port model abnormal diagnosis provided according to an optional embodiment of the present application, as shown in Figure 4 , the comprehensive line loss power supply port model information is input, and combined with the model abnormal diagnosis model, it is judged whether there is a case that the power supply port is not configured with a sub-pressure port. If there is a power supply port model abnormality, the power supply port model abnormality details are output. Figure 5 is a flowchart of power supply port metering abnormal diagnosis provided according to an optional embodiment of the present application, as shown in Figure 5 , the comprehensive line loss power supply port metering information is input, and combined with the metering abnormal diagnosis model, it is judged whether there is a case that the table bottom is missing, the table bottom is reversed, the power supply port electricity jumps, etc. If there is a power supply port metering abnormality, the power supply port metering abnormality details are output.

[0051] , the table bottom is missing, the table bottom is reversed, the power supply port electricity jumps, etc. If there is a power supply port metering abnormality, the power supply port metering abnormality details are output.

[0052] As an optional embodiment, based on the abnormal node, the plurality of target nodes are determined, including: taking the abnormal node as a first node, constructing an initial queue; taking the first node from the initial queue, judging whether the first node is a target node; in the case that the first node is not a target node, adding all the remaining nodes adjacent to the first node to the initial queue; repeating the above process until there is no node in the initial queue, determining the plurality of target nodes.

[0053] Optionally, a Breadth-First Search (BFS) algorithm can be used to determine the influence range of the line loss abnormal node, that is, to find all the target nodes directly or indirectly electrically connected with the abnormal node. Specifically, the abnormal node is taken as a starting point of the search and is put into an initial queue. The queue is a first-in first-out data structure, which means that the node put in first will be processed first. The first node is taken from the constructed initial queue, that is, the initial abnormal node. It is judged whether the taken first node meets the condition of the target node. The target node refers to those nodes associated with the abnormal node. If the first node is exactly the target node, the relevant information of the node is recorded. If the taken first node is not the target node, the nodes adjacent to it need to be further explored. All the nodes directly connected with the current node are added to the queue, which guarantees that the search is carried out according to the direct electrical connection relationship between the nodes, in line with the principle of breadth first. The above process is repeated until the queue is empty, that is, all the nodes have been checked once. Each time the next node is taken from the queue, it is checked whether it is a target node; if not, its adjacent nodes are continuously added to the queue. Such progressive search ensures that all the nodes directly or indirectly connected with the abnormal node can be traversed. Through the Breadth-First Search algorithm, all the target nodes directly or indirectly electrically connected with the abnormal node can be finally determined.

[0054] For example, taking the abnormal node as a power supply interface, taking the 202A power supply interface as the starting node v0 to be searched, an empty queue Q is constructed, and the starting node is added to the queue, that is, Q=[v0]. When the queue Q is not empty, a loop operation is performed, which is recorded as while(Q≠[]). In each loop, the head node v is taken from the queue Q, which is recorded as v=Q.pop(0). The taken node v is checked to judge whether it is a target node (in this optional embodiment, the node data is checked whether it is a node associated with the 202A power supply interface). If the node v is not a target node (not affected by the 202A power supply interface), all its unvisited adjacent nodes u are added to the queue Q, that is, Q.append(u). The above process is repeated until the queue is empty, and the breadth-first search of all nodes is completed.

[0055] In addition, the breadth-first algorithm can not only determine the target nodes associated with the abnormal node, but also output the related abnormal conditions of the target nodes. For example, taking the power supply node as an example, first, the integrated line loss power supply node metering point is used to find the corresponding integrated line loss node, the node is decomposed according to the node attribute, and the unit's sub-voltage line loss of the power supply side and the unit's sub-voltage line loss of the power receiving side are output. Then, the power supply node metering point is associated with the sub-component line loss model, and the affected substation, bus, transmission line and other equipment line loss conditions are determined through the model information, and the line loss conditions of the affected equipment are output.

[0056] Through the breadth-first search algorithm, the sub-voltage node and the sub-component node information affected by the power supply node can be determined, the affected units and the affected sub-voltage line loss rate of a certain voltage level of the sub-voltage node are determined through the sub-voltage node property and the power supply and power receiving unit information, and the sub-component line loss rate can be determined through the sub-component node information. The line loss linkage analysis conclusion is determined, and the line loss range affected by the power supply node is output. When the power supply node power is abnormal, it will affect the substation line loss, the sub-voltage line loss of a certain voltage level of the unit, the sub-voltage line loss of a certain voltage level of the power supply unit, and the sub-voltage line loss of a certain voltage level of the unit.

[0057] As an optional embodiment, based on the abnormal operation data, the estimated operation data is estimated, including: based on the abnormal operation data, a plurality of historical operation data is determined, wherein the plurality of historical operation data is the operation data corresponding to a plurality of dates before the day when the abnormal operation data is generated, and the interval between the plurality of dates is one week; the average value of the plurality of historical operation data is determined as the estimated operation data.

[0058] Optionally, the operation data of a series of historical dates is selected by calculating forward from the day when the abnormal operation data is generated. Generally, the average value of a series of historical date operation data can be taken as the estimated operation data, for example, taking the power supply node power data as an example, the estimated operation data can be:

[0059]

[0060] Wherein, E i+1 is the power of node i+1 day, unit: kWh; E i is the power of node per day, unit: kWh; n is the number of days participating in the average power calculation. However, the power data is greatly affected by the weekend, and the simple average method does not consider the influence of the weekend, so the periodic mean method can be used to eliminate the weekend factor, that is, the interval between a series of historical dates is set to one week, that is, the operation data of the corresponding dates one week ago, two weeks ago, three weeks ago, etc. The final estimated operation data (taking the power supply node power data as an example) is calculated as follows:

[0061]

[0062] wherein, E t is the corrected power of the day, in kWh; E i is the power of the day pushed back by i days, i=t-7, t-14, t-21, etc.; n is the number of nodes participating in the average power calculation.

[0063] As an optional embodiment, the abnormality detection result of the power system is determined based on the calculated line loss rate, including: respectively calculating the difference between the calculated line loss rate of each of the abnormal node and the plurality of target nodes and the corresponding standard line loss rate to obtain a plurality of initial differences; comparing the sizes of the plurality of initial differences to determine that the initial difference exceeding the second preset threshold in the plurality of initial differences is a target difference; and determining the abnormality detection result based on the target difference.

[0064] Optionally, the standard line loss rate is a line loss benchmark value obtained according to the design parameters of the power system, the rated operating conditions, and related industry standards or historical data statistics. For the abnormal node and each target node, the difference between the calculated line loss rate and the corresponding standard line loss rate is calculated to obtain a set of initial differences. These differences reflect the degree of deviation of the line loss rate of each node from the standard situation. By comparing the sizes of all initial differences, those initial differences exceeding the second preset threshold are determined as target differences. Based on the analysis of the target differences, it can be further determined which nodes have abnormally serious line loss rates and need to be paid attention to and handled in priority, and the abnormality detection result of the power system can be determined, which includes the identified abnormal node and its specific impact on the system line loss rate, and which additional target nodes also have abnormal line loss situations. The abnormality detection result not only points out the location of the problem, but also provides detailed data support for subsequent line loss abnormality diagnosis and treatment.

[0065] Taking the power supply port as an example, the calculated port power is substituted into the associated voltage division node to calculate whether the line loss rate of the voltage division is restored to normal, and the port power data before and after the calculation, and the voltage division line loss rate before and after the calculation are output. The nodes such as substations and buses affected by the port operation are consistent with the above. The port power data before and after the calculation, the comprehensive line loss rate before and after the calculation, and the affected voltage division line loss rate, substation line loss rate and bus balance rate are output. Through the power data after the calculation, the pulling of the port power on the change of the comprehensive line loss rate is calculated, the influence of the port on the comprehensive line loss is evaluated, the dominant factor is determined, and finally the influence conclusion of the power supply port is output to assist the user in line loss analysis and management.

[0066] As an optional embodiment, a comprehensive line loss abnormality diagnosis method based on breadth-first search is also provided, Figure 6 is a flowchart of the comprehensive line loss abnormality diagnosis method based on breadth-first search provided according to an optional embodiment of the present application, as Figure 6As shown, first, it is checked whether the comprehensive line loss rate is within the deviation range, if not within the threshold range, an abnormality checking process is started, and the comprehensive line loss abnormal data details are output by the line loss diagnosis analysis model. Then, the breadth first search algorithm is used, the abnormal data details are taken as the starting point, the abnormal data influence range is determined, finally, the comprehensive line loss is checked whether it is restored to normal through the power automatic fitting calculation, and the main influencing factor of the comprehensive line loss is determined through the abnormal point power data.

[0067] Taking a specific power supply interface node as an example, Figure 7 The flow chart of the power supply interface power abnormality diagnosis method provided according to the optional embodiment of the application is shown in the figure, Figure 7 As shown, the judgment data corresponding to the power supply interface is power, the past interface or user power abnormality can only view the current affected comprehensive line loss, and cannot comprehensively view the affected range, increasing the artificial analysis workload. The breadth first search algorithm is used to comprehensively traverse all nodes of the comprehensive line loss calculation, output all line loss abnormal nodes of the comprehensive line loss, improve the comprehensiveness and accuracy of the line loss abnormality diagnosis, avoid the possible missing abnormal points of the traditional method, reduce the line loss analysis difficulty, and improve the line loss management efficiency.

[0068] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the application is not limited by the action sequence described, because according to the application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the application.

[0069] Through the description of the above embodiments, those skilled in the art can clearly understand that the line loss abnormality detection method for the power system according to the above embodiments can be realized by means of software and the necessary general hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the application can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), includes a plurality of instructions to make a terminal device (which can be a mobile phone, computer, server, or network device, etc.) execute the method described in each embodiment of the application.

[0070] According to the embodiment of the application, a device for implementing the line loss abnormality detection method for the power system is also provided, Figure 8 The structural block diagram of the device provided according to the embodiment of the application is shown in the figure, Figure 8As shown, the apparatus includes an acquisition module 81, a first determination module 82, a first estimation module 83, a second estimation module 84, and a second determination module 85, which are described below.

[0071] The acquisition module 81 is configured to acquire an abnormal node in a power system and abnormal operation data corresponding to the abnormal node, where the abnormal node is a node with abnormal line loss rate.

[0072] The first determination module 82 is connected with the acquisition module 81 and configured to determine a plurality of target nodes based on the abnormal node, where the target node is a node associated with the abnormal node.

[0073] The first estimation module 83 is connected with the first determination module 82 and configured to estimate pseudo-calculated operation data based on the abnormal operation data, where the pseudo-calculated operation data is operation data generated under normal conditions.

[0074] The second estimation module 84 is connected with the first estimation module 83 and configured to estimate a pseudo-calculated line loss rate of the abnormal node and a pseudo-calculated line loss rate of each of the plurality of target nodes based on the pseudo-calculated operation data, where the pseudo-calculated line loss rate is a corresponding line loss rate under normal conditions.

[0075] The second determination module 85 is connected with the second estimation module 84 and configured to determine an abnormal detection result of the power system based on the pseudo-calculated line loss rate, where the abnormal detection result includes a line loss abnormal factor of the power system.

[0076] It should be noted that the acquisition module 81, the first determination module 82, the first estimation module 83, the second estimation module 84, and the second determination module 85 correspond to steps S201 to S205 in the above embodiment, and the plurality of modules have the same instances and application scenarios as the corresponding steps, but are not limited to the above disclosed contents. It should be noted that the above modules as part of the apparatus can run in the computer terminal 10 provided in the above embodiment.

[0077] The embodiment of the present application can provide a computer device. Optionally, in the embodiment, the computer device can be located in at least one network device of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0078] The memory can be configured to store software programs and modules, such as program instructions / modules corresponding to the line loss anomaly detection method and device for a power system in the embodiments of the present application. The processor executes the software programs and modules stored in the memory to perform various functional applications and data processing, i.e., to implement the line loss anomaly detection method for a power system described above. The memory can include a high-speed random access memory, and can further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory can further include a memory remotely disposed relative to the processor, which can be connected to a computer terminal through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0079] The processor can call information and applications stored in the memory through the transmission device to perform the following steps: acquiring an abnormal node in the power system and abnormal operation data corresponding to the abnormal node, wherein the abnormal node is a node with abnormal line loss rate; determining a plurality of target nodes based on the abnormal node, wherein the target node is a node associated with the abnormal node; estimating the pseudo-calculated operation data based on the abnormal operation data, wherein the pseudo-calculated operation data is operation data generated under normal circumstances; respectively estimating the pseudo-calculated line loss rate of the abnormal node and the pseudo-calculated line loss rate of each of the plurality of target nodes based on the pseudo-calculated operation data, wherein the pseudo-calculated line loss rate is the corresponding line loss rate under normal circumstances; determining an abnormal detection result of the power system based on the pseudo-calculated line loss rate, wherein the abnormal detection result includes a line loss anomaly factor of the power system.

[0080] Optionally, the processor can further execute program codes of the following steps: acquiring an abnormal node in the power system, including: acquiring a comprehensive line loss rate of the power system, wherein the comprehensive line loss rate is determined based on power supply port power, distributed power supply power, high-voltage user power, and low-voltage user power; determining whether the comprehensive line loss rate exceeds a first preset threshold, wherein the first preset threshold is determined based on the comprehensive line loss rate of the power system within a preset historical time; and determining the abnormal node in the case where the comprehensive line loss rate exceeds the first preset threshold.

[0081] Optionally, the processor can further execute program codes of the following steps: acquiring abnormal operation data corresponding to the abnormal node, including: acquiring operation data corresponding to each of a plurality of dimensions of the abnormal node, wherein the plurality of dimensions include profile information dimension, model topology dimension, metering data dimension, and electricity stealing data dimension; respectively determining whether the operation data corresponding to each of the plurality of dimensions is abnormal, and taking the operation data with abnormality as the abnormal operation data.

[0082] Optionally, the processor can further execute program codes of the following steps: determining the plurality of target nodes based on the abnormal node, comprising: taking the abnormal node as a first node to construct an initial queue; taking the first node from the initial queue to determine whether the first node is a target node; in the case that the first node is not a target node, adding all the remaining nodes adjacent to the first node to the initial queue; repeating the above process until there is no node in the initial queue to determine the plurality of target nodes.

[0083] Optionally, the processor can further execute program codes of the following steps: estimating the estimated running data based on the abnormal running data, comprising: determining a plurality of historical running data based on the abnormal running data, wherein the plurality of historical running data are running data corresponding to a plurality of dates before the day when the abnormal running data is generated, and the interval between the plurality of dates is one week; determining the average of the plurality of historical running data as the estimated running data.

[0084] Optionally, the processor can further execute program codes of the following steps: determining the abnormal detection result of the power system based on the estimated line loss rate, comprising: calculating the difference between the estimated line loss rate of each of the abnormal node and the plurality of target nodes and the corresponding standard line loss rate to obtain a plurality of initial differences; comparing the sizes of the plurality of initial differences to determine that the initial difference exceeding the second preset threshold in the plurality of initial differences is a target difference; determining the abnormal detection result based on the target difference.

[0085] By adopting the embodiment of the present application, a line loss abnormality detection method for a power system is provided. The abnormal node in the power system and the abnormal running data corresponding to the abnormal node are obtained, wherein the abnormal node is a node with abnormal line loss rate; based on the abnormal node, a plurality of target nodes are determined, wherein the target node is a node associated with the abnormal node; based on the abnormal running data, an estimated running data is estimated, wherein the estimated running data is running data generated under normal circumstances; based on the estimated running data, an estimated line loss rate of the abnormal node and an estimated line loss rate of each of the plurality of target nodes are respectively estimated, wherein the estimated line loss rate is a corresponding line loss rate under normal circumstances; based on the estimated line loss rate, an abnormal detection result of the power system is determined, wherein the abnormal detection result includes a line loss abnormality factor of the power system, thereby achieving the purpose of accurately determining the line loss abnormality reason of the power system, and realizing the technical effect of improving the efficiency and safety of the power system operation, and further solving the technical problem that the traditional manual investigation method is not accurate and fast enough in positioning the abnormal reason.

[0086] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiments can be completed by instructing the terminal device related hardware through a program, and the program can be stored in a non-volatile storage medium, which can include a flash disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0087] The embodiments of the present application also provide a non-volatile storage medium. Optionally, in the present embodiment, the non-volatile storage medium can be used to save the program code executed by the line loss anomaly detection method for power systems provided by the above-mentioned embodiments.

[0088] Optionally, in the present embodiment, the non-volatile storage medium can be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

[0089] Optionally, in the present embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining an abnormal node in a power system and abnormal operation data corresponding to the abnormal node, wherein the abnormal node is a node with abnormal line loss rate; determining a plurality of target nodes based on the abnormal node, wherein the target node is a node associated with the abnormal node; estimating the pseudo-calculated operation data based on the abnormal operation data, wherein the pseudo-calculated operation data is the operation data generated under normal circumstances; estimating the pseudo-calculated line loss rate of the abnormal node and the pseudo-calculated line loss rate of each of the plurality of target nodes based on the pseudo-calculated operation data, wherein the pseudo-calculated line loss rate is the corresponding line loss rate under normal circumstances; determining an abnormal detection result of the power system based on the pseudo-calculated line loss rate, wherein the abnormal detection result includes the line loss anomaly factor of the power system.

[0090] Optionally, in the present embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining an abnormal node in a power system, including: obtaining a comprehensive line loss rate of the power system, wherein the comprehensive line loss rate is determined based on the power supply gateway power, the distributed power supply power, the high-voltage user power and the low-voltage user power; determining whether the comprehensive line loss rate exceeds a first preset threshold, wherein the first preset threshold is determined based on the comprehensive line loss rate of the power system in a preset historical time; and determining the abnormal node in the case that the comprehensive line loss rate exceeds the first preset threshold.

[0091] Optionally, in the embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining the abnormal running data corresponding to the abnormal node, comprising: obtaining the running data corresponding to each of a plurality of dimensions of the abnormal node, wherein the plurality of dimensions comprise an archive information dimension, a model topology dimension, a metering data dimension, and a electricity stealing data dimension; and respectively determining whether the running data corresponding to each of the plurality of dimensions is abnormal, and taking the running data that is abnormal as the abnormal running data.

[0092] Optionally, in the embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining a plurality of target nodes based on the abnormal node, comprising: taking the abnormal node as a first node to construct an initial queue; taking the first node from the initial queue to determine whether the first node is a target node; in a case where the first node is not a target node, adding all remaining nodes adjacent to the first node to the initial queue; and repeating the above process until there is no node in the initial queue, and determining the plurality of target nodes.

[0093] Optionally, in the embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: estimating the estimated running data based on the abnormal running data, comprising: determining a plurality of historical running data based on the abnormal running data, wherein the plurality of historical running data are running data corresponding to a plurality of dates before the day when the abnormal running data is generated, and the interval between the plurality of dates is one week; and determining an average value of the plurality of historical running data as the estimated running data.

[0094] Optionally, in the embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining an abnormal detection result of the power system based on the estimated line loss rate, comprising: respectively calculating a difference between the estimated line loss rate of each of the abnormal node and the plurality of target nodes and the corresponding standard line loss rate to obtain a plurality of initial differences; comparing the sizes of the plurality of initial differences to determine that an initial difference exceeding a second preset threshold in the plurality of initial differences is a target difference; and determining the abnormal detection result based on the target difference.

[0095] The embodiment of the present application further provides a computer program product comprising a computer program, which, when executed by a processor, can realize the following: acquiring an abnormal node in a power system and abnormal operation data corresponding to the abnormal node, wherein the abnormal node is a node with abnormal line loss rate; determining a plurality of target nodes based on the abnormal node, wherein the target node is a node associated with the abnormal node; estimating pseudo-calculated operation data based on the abnormal operation data, wherein the pseudo-calculated operation data is operation data generated under normal conditions; respectively estimating pseudo-calculated line loss rates of the abnormal node and the plurality of target nodes based on the pseudo-calculated operation data, wherein the pseudo-calculated line loss rate is a corresponding line loss rate under normal conditions; and determining an abnormal detection result of the power system based on the pseudo-calculated line loss rates, wherein the abnormal detection result comprises a line loss abnormal factor of the power system.

[0096] The above-mentioned serial numbers of the embodiments of the present application are only for description, and do not represent the advantages or disadvantages of the embodiments.

[0097] In the above-mentioned embodiments of the present application, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0098] In several embodiments provided in the present application, it should be understood that the disclosed technical contents can be implemented by other ways. Among them, the above-mentioned device embodiments are only schematic, for example, the division of the units can be a logical function division, and actual implementation can have another division way, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or modules shown or discussed can be indirect coupling or communication connection through some interfaces, units or modules, which can be electrical or other forms.

[0099] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed to a plurality of units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0100] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be realized in the form of hardware or in the form of software functional unit.

[0101] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a nonvolatile storage medium. Based on such understanding, the technical solutions of the present application, essentially or in part, or all or part of the technical solutions, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, and various media that can store program codes.

[0102] The above description is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A line loss anomaly detection method for a power system, characterized by, The method comprises: acquiring an abnormal node in a power system and abnormal operation data corresponding to the abnormal node, wherein the abnormal node is a node with abnormal line loss rate; determining a plurality of target nodes based on the abnormal node, wherein the target node is a node associated with the abnormal node; estimating pseudo-calculated operation data based on the abnormal operation data, wherein the pseudo-calculated operation data is operation data generated under normal circumstances; estimating a pseudo-calculated line loss rate of the abnormal node and a pseudo-calculated line loss rate of each of the plurality of target nodes based on the pseudo-calculated operation data, wherein the pseudo-calculated line loss rate is a corresponding line loss rate under normal circumstances; determining an abnormal detection result of the power system based on the pseudo-calculated line loss rate, wherein the abnormal detection result includes a line loss abnormal factor of the power system.

2. The method of claim 1, wherein, The acquiring of the abnormal node in the power system comprises: acquiring a comprehensive line loss rate of the power system, wherein the comprehensive line loss rate is determined based on power supply gate power, distributed power supply power, high-voltage user power, and low-voltage user power; determining whether the comprehensive line loss rate exceeds a first preset threshold, wherein the first preset threshold is determined based on the comprehensive line loss rate of the power system within a preset historical time; in a case where the comprehensive line loss rate exceeds the first preset threshold, determining the abnormal node.

3. The method of claim 1, wherein, The acquiring of the abnormal operation data corresponding to the abnormal node comprises: acquiring operation data corresponding to each of a plurality of dimensions of the abnormal node, wherein the plurality of dimensions include an archive information dimension, a model topology dimension, a metering data dimension, and a electricity stealing data dimension; determining whether the operation data corresponding to each of the plurality of dimensions is abnormal, and taking the operation data with abnormality as the abnormal operation data.

4. The method of claim 1, wherein, The determining of the plurality of target nodes based on the abnormal node comprises: taking the abnormal node as a first node to construct an initial queue; taking the first node from the initial queue to determine whether the first node is the target node; in a case where the first node is not the target node, adding all remaining nodes adjacent to the first node to the initial queue; repeating the above process until there is no node in the initial queue, and determining the plurality of target nodes.

5. The method of claim 1, wherein, The estimating of the pseudo-calculated operation data based on the abnormal operation data comprises: determining a plurality of historical operation data based on the abnormal operation data, wherein the plurality of historical operation data is operation data corresponding to a plurality of dates before the day when the abnormal operation data is generated, and the interval between the plurality of dates is one week; determining an average value of the plurality of historical operation data as the pseudo-calculated operation data.

6. The method according to any one of claims 1 to 5, characterized in that, The determining of the abnormal detection result of the power system based on the pseudo-calculated line loss rate comprises: calculating a difference value between the pseudo-calculated line loss rate of each of the abnormal node and the plurality of target nodes and a corresponding standard line loss rate to obtain a plurality of initial difference values; comparing the plurality of initial difference values to determine an initial difference value exceeding a second preset threshold in the plurality of initial difference values as a target difference value; determining the abnormal detection result based on the target difference value.

7. A line loss anomaly detection apparatus for a power system, characterized by, The method comprises: An acquisition module is configured to acquire an abnormal node in a power system and abnormal operation data corresponding to the abnormal node, wherein the abnormal node is a node with abnormal line loss rate; A first determination module is configured to determine a plurality of target nodes based on the abnormal node, wherein the target node is a node associated with the abnormal node; A first estimation module is configured to estimate pseudo-calculated operation data based on the abnormal operation data, wherein the pseudo-calculated operation data is operation data generated under normal conditions; A second estimation module is configured to estimate a pseudo-calculated line loss rate of the abnormal node and a pseudo-calculated line loss rate of each of the plurality of target nodes based on the pseudo-calculated operation data, wherein the pseudo-calculated line loss rate is a corresponding line loss rate under normal conditions; A second determination module is configured to determine an abnormal detection result of the power system based on the pseudo-calculated line loss rate, wherein the abnormal detection result includes a line loss abnormal factor of the power system.

8. A non-volatile storage medium, comprising: The non-volatile storage medium includes a stored program, wherein the program controls the device in which the non-volatile storage medium is located to execute the line loss abnormality detection method for a power system according to any one of claims 1 to 6 when the program is running.

9. A computer device, comprising: Comprise: A memory and a processor, The memory stores a computer program; The processor is configured to execute the computer program stored in the memory, and the computer program makes the processor execute the line loss abnormality detection method for a power system according to any one of claims 1 to 6 when running.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the line loss abnormality detection method for a power system according to any one of claims 1 to 6.

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