Voltage sag source positioning method based on pattern matching and related device

Abstract

The invention discloses a voltage sag source positioning method based on pattern matching and a related device. The method comprises the following steps: acquiring s historical fault events occurring in a preset historical time period of a power system; determining corresponding s voltage sag amplitude characteristics and voltage sag duration characteristics according to the s historical fault events; constructing a first feature mode library according to the s voltage sag amplitude features and the voltage sag duration features; acquiring k pieces of monitoring data of the k monitoring devices, and determining a target voltage sag mode according to the k pieces of monitoring data; and determining a target fault event according to the target voltage sag mode and the first characteristic mode library. According to the invention, accurate voltage sag source positioning can be carried out in a mode of mode matching.

Description

Technical Field

[0001] The present application relates to the technical field of voltage sag source location, and in particular, to a voltage sag source location method based on pattern matching and related devices. Background Art

[0002] Voltage sag source location refers to the process of quickly and accurately finding the fault source that causes voltage sags in a power system through voltage sag tracing technology. By quickly and accurately identifying and isolating the sag source, the system operation and maintenance duration and maintenance cost can be effectively reduced, and the user experience and service quality can be significantly improved.

[0003] With the development of artificial intelligence technology, existing sag source location methods are based on artificial intelligence methods for sag source location, but a large amount of training data is required to train the model, and the quality requirements for the data are relatively high. If the data is noisy or incomplete, it may affect the performance of the model. Moreover, in order to comprehensively understand the voltage sag situation, a large number of monitoring points need to be widely arranged, which will also increase the cost and complexity.

[0004] Therefore, how to more accurately locate the voltage sag source to identify information such as the specific location of the fault has become an urgent problem to be solved. Summary of the Invention

[0005] The embodiments of the present application provide a voltage sag source location method based on pattern matching and related devices. By obtaining a plurality of historical fault events in a preset historical time period of a power system, calculating the voltage sag amplitude characteristics and voltage sag duration characteristics corresponding to the power system according to the plurality of historical fault events, constructing a feature pattern library based on the voltage sag amplitude characteristics and voltage sag duration characteristics, and then matching the target voltage sag pattern determined by the real-time monitoring data of the monitoring device with the feature pattern library, accurate and efficient location of the voltage sag source is realized.

[0006] In a first aspect, the embodiments of the present application provide a voltage sag source location method based on pattern matching, which is applied to a control device of a power system. The power system further includes: n nodes, p sensitive load access points, and k monitoring devices, where n, p, and k are all positive integers. The method includes:

[0007] Obtaining s historical fault events that occurred in a preset historical time period of the power system; the historical fault events include: voltage sag duration, the pre-fault three-phase voltages of the p sensitive load access points, the fault node, the fault type corresponding to the fault node, and the pre-fault three-phase voltages of the fault node; s is a positive integer;

[0008] Determining voltage sag amplitude characteristics according to each of the s historical fault events to obtain s voltage sag amplitude characteristics;

[0009] Determine the voltage sag duration characteristics corresponding to the s historical fault events according to the preset probability distribution of voltage sag duration;

[0010] Construct a first feature pattern library according to the s voltage sag amplitude characteristics and the voltage sag duration characteristics; the first feature pattern library includes s voltage sag patterns, each voltage sag pattern corresponding to a voltage sag amplitude characteristic and a voltage sag duration; each voltage sag pattern corresponds to a fault event;

[0011] Obtain the k monitoring data of the k monitoring devices, and determine the target voltage sag pattern according to the k monitoring data;

[0012] Determine the target fault event according to the target voltage sag pattern and the first feature pattern library.

[0013] In a second aspect, an embodiment of the present application provides a voltage sag source localization device based on pattern matching, which is applied to a control device of a power system. The power system further includes: n nodes, p sensitive load access points, and k monitoring devices, where n, p, and k are all positive integers; the voltage sag source localization device based on pattern matching includes: a first data acquisition module, a first feature determination module, a second feature determination module, a feature pattern library construction module, a second data acquisition module, and a fault localization module, where,

[0014] The first data acquisition module is configured to obtain s historical fault events that occurred in a preset historical time period of the power system; the historical fault events include: voltage sag duration, pre-fault three-phase voltages of the p sensitive load access points, fault nodes, fault types corresponding to the fault nodes, and pre-fault three-phase voltages of the fault nodes; s is a positive integer;

[0015] The first feature determination module is configured to determine voltage sag amplitude characteristics according to each of the s historical fault events, and obtain s voltage sag amplitude characteristics;

[0016] The second feature determination module is configured to determine the voltage sag duration characteristics corresponding to the s historical fault events according to the preset probability distribution of voltage sag duration;

[0017] The feature pattern library construction module is configured to construct a first feature pattern library according to the s voltage sag amplitude characteristics and the voltage sag duration characteristics; the first feature pattern library includes s voltage sag patterns, each voltage sag pattern corresponding to a voltage sag amplitude characteristic and a voltage sag duration; each voltage sag pattern corresponds to a fault event;

[0018] The second data acquisition module is configured to obtain k monitoring data of the k monitoring devices, and determine a target voltage sag mode according to the k monitoring data;

[0019] The fault location module is configured to determine a target fault event according to the target voltage sag mode and the first feature pattern library.

[0020] In a third aspect, an embodiment of the present application provides an electronic device, including: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing the steps in the first aspect of the embodiments of the present application.

[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program causes a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application.

[0022] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0023] It can be seen that by adopting the embodiments of the present application, the following beneficial effects are achieved:

[0024] By implementing the embodiments of the present application, s historical fault events occurring in a preset historical time period of the power system are obtained; a voltage sag amplitude characteristic is determined according to each of the s historical fault events, and s voltage sag amplitude characteristics are obtained; a voltage sag duration characteristic corresponding to the s historical fault events is determined according to a preset voltage sag duration probability distribution; a first feature pattern library is constructed according to the s voltage sag amplitude characteristics and the voltage sag duration characteristics; k monitoring data of k monitoring devices are obtained, and a target voltage sag mode is determined according to the k monitoring data; a target fault event is determined according to the target voltage sag mode and the first feature pattern library. It can be seen that by constructing a feature pattern library through historical fault events and performing precise voltage sag source location through pattern matching. Description of the Drawings

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background art, the following will describe the drawings required to be used in the embodiments of the present application or the background art.

[0026] Figure 1 It is a schematic structural diagram of a power system provided by an embodiment of the present application;

[0027] Figure 2 It is a schematic structural diagram of a power system line provided by an embodiment of the present application;

[0028] Figure 3 It is a schematic flowchart of a voltage sag source location method based on pattern matching provided by an embodiment of the present application;

[0029] Figure 4 It is another schematic structural diagram of a power system provided by an embodiment of the present application;

[0030] Figure 5 It is a schematic diagram of the construction of a second feature pattern library provided by an embodiment of the present application;

[0031] Figure 6 It is an application scenario diagram of a pattern matching method provided by an embodiment of the present application;

[0032] Figure 7 It is a schematic structural diagram of a voltage sag source location device based on pattern matching provided by an embodiment of the present application;

[0033] Figure 8 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0034] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0035] Terms such as "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. 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 that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0036] References to "embodiments" in this document mean that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0037] The following explains the relevant content, concepts, meanings, technical problems, technical solutions, beneficial effects, etc. involved in the embodiments of the present application.

[0038] Please refer to Figure 1 , Figure 1 which is a schematic structural diagram of a power system provided by an embodiment of the present application. As shown in the figure, the power system includes a control device, n nodes, p sensitive load access points, and k monitoring devices, where n, p, and k are all positive integers.

[0039] A power system is an electric energy production and consumption system composed of links such as power generation, transmission, transformation, distribution, and power consumption. It can organically connect different functional parts through various electrical equipment and lines to achieve efficient transmission, distribution, and use of electric energy, aiming to provide safe, reliable, and qualified electric energy for various users.

[0040] A control device is a device or system used to adjust and manage the operating state of a power system. It has functions of signal acquisition, processing, and analysis. By monitoring the operating parameters of the power system, such as voltage, current, frequency, etc., and according to preset control strategies and algorithms, it generates control instructions, which are used to adjust and control the power system to ensure that the power system can operate stably, reliably, and economically under different working conditions.

[0041] A node is the intersection point of electrical connections in a power system, representing a specific position in the power network. At the node, the distribution and transmission of electric energy from the transmission line to the distribution line or other electrical equipment are realized. Each node has specific electrical parameters, such as voltage amplitude, voltage phase angle, etc. The changes in these parameters reflect the operating state of the power system.

[0042] A sensitive load access point is an interface position in a power system specifically used to connect load equipment that is sensitive to changes in power quality. Sensitive loads usually refer to electrical equipment that is more sensitive to power quality problems such as voltage sags, voltage fluctuations, and frequency deviations, such as electronic computers, precision electronic instruments, medical equipment, etc. These devices may experience operating failures, performance degradation, data loss, etc. when the power quality is poor. Therefore, the sensitive load access points can be set by comprehensively considering the layout of the power system, load characteristics, and power quality guarantee measures to ensure that the sensitive loads can obtain power supply that meets their operating requirements.

[0043] It should be noted that in a power system, a node may be connected to multiple sensitive load access points. For example, in some large commercial buildings or industrial plants, multiple devices or systems sensitive to power quality are connected to the power supply node, such as sophisticated electronic production equipment, large computer server clusters, etc. Each of these devices corresponds to a sensitive load access point, that is, one node corresponds to multiple sensitive load access points. In this scenario, the number of sensitive loads is large and they are concentrated. To ensure the normal operation of these loads, multiple access points need to be set at the same node. Also, multiple nodes may correspond to one sensitive load access point. For example, for large sensitive load devices (such as the core computer room of a data center), due to their large power demand and extremely high requirements for power supply reliability, multiple power supplies are used to introduce power from multiple nodes to form a total sensitive load access point at the device end to prevent the device from shutting down due to a single node failure. Of course, in some simple power system scenarios or small-scale sensitive load access situations, there is also a case where one node corresponds to one sensitive load access point. For example, in a small precision laboratory, only a set of experimental equipment sensitive to voltage sags is equipped and powered from a nearby node. At this time, it is a one-to-one correspondence.

[0044] In the embodiments of this application, the case where multiple nodes correspond to one sensitive load access point is adopted. By analyzing one sensitive load access point, the voltage sag conditions of the multiple nodes corresponding to this sensitive load access point can be analyzed. Of course, for the convenience of calculation, the case where one node corresponds to one sensitive load access point can also be adopted. At this time, by calculating the voltage sag amplitude of a certain sensitive load access point, the voltage sag condition of the corresponding node can be determined.

[0045] A monitoring device is a device installed at specific locations in a power system (such as nodes, sensitive load access points) for real-time collection and recording of the operating parameters of the power system. It can perform high-precision measurements on electrical quantities such as voltage, current, power, and frequency, and store, process, and transmit the measurement data. The monitoring device can adopt various sensor technologies (such as voltage transformers, current transformers, etc.) and data acquisition and processing systems, and has the characteristics of high reliability, high accuracy, and high real-time performance. The collected data can be used for multiple aspects such as the operating state monitoring, fault diagnosis, and power quality assessment of the power system.

[0046] Please refer to Figure 2 , Figure 2It is a schematic structural diagram of a power system line provided by an embodiment of the present application. As shown in the figure, the power system line is a part of the power system. There are multiple nodes on this power system line. Sensitive load access points can be set at some nodes. At the same time, monitoring devices can be set at the sensitive load access points. Of course, monitoring devices can also be set at other nodes. In the embodiment of the present application, the monitoring device is set at the sensitive load access point because the sensitive load access point is used as a key node for data collection and analysis.

[0047] Please refer to Figure 3 , Figure 3 It is a schematic flowchart of a voltage sag source location method based on pattern matching provided by an embodiment of the present application. This method is applied to the control device of the power system. The power system further includes: n nodes, p sensitive load access points, and k monitoring devices, where n, p, and k are all positive integers. This method includes but is not limited to the following steps:

[0048] S101. Obtain s historical fault events that occurred in the preset historical time period of the power system.

[0049] In the embodiment of the present application, the preset historical time period is a pre-set past time interval, which is used to indicate that the power system collects relevant fault data during this time period. For example, the preset historical time period can be set to the past 3 years. By collecting the fault events that occurred in the power system within the past 3 years, the fault events that occurred within these 3 years include information such as different types of short-circuit faults.

[0050] In the embodiment of the present application, the power system usually has a monitoring system. This monitoring system includes multiple monitoring points. The monitoring points can be acquisition devices such as sensors and monitoring equipment distributed at different locations. The monitoring points can continuously monitor the operating state of the power system. Once a fault occurs, relevant fault data will be recorded.

[0051] In a specific embodiment, s historical fault events that occurred in the preset historical time period of the power system can be obtained through the monitoring system of the power system. Among them, the historical fault events include: the voltage sag duration, the pre-fault three-phase voltages of the p sensitive load access points, the fault nodes among the n nodes, the fault types corresponding to the fault nodes, and the pre-fault three-phase voltages of the fault nodes. Where s is a positive integer.

[0052] It should be noted that the voltage sag duration refers to the time length from when the voltage starts to drop to when it returns to the normal level when a fault occurs in the power system, that is, when a voltage sag phenomenon appears. Among them, the normal level is a pre-set standard voltage value, such as the rated voltage. The voltage sag duration can reflect the influence degree of the voltage sag on the power system.

[0053] A sensitive load refers to an electrical load that is sensitive to voltage fluctuations, and a voltage sag may cause abnormal operation. The access point of a sensitive load refers to the location where the sensitive load is connected to the power system, and there are p access points of sensitive loads in the power system. The voltage values of the three phases A, B, and C at the access point of the sensitive load can be collected through the monitoring system. The three-phase voltage before a fault refers to the voltage level at the access point of the sensitive load when the power system is operating normally before the fault occurs.

[0054] A node can be a substation bus, a line connection point, or a load access point in the power system, which is not limited here, and the power system includes n nodes. A fault node refers to a node that shows abnormal conditions when a fault occurs in the power system. The abnormal conditions can be abnormal voltage amplitude, abnormal voltage phase, abnormal current, abnormal power factor, etc. If a node shows abnormal conditions, it can be determined that the node is a fault node. For example, when the voltage amplitude of a certain node drops significantly, such as below 80%-90% of the rated voltage, or the voltage amplitude rises significantly, such as above 110%-120% of the rated voltage, it can be determined that the node shows abnormal conditions.

[0055] There are various types of faults in the power system, such as short-circuit faults and open-circuit faults. Among them, short-circuit faults include single-phase short circuits, three-phase short circuits, two-phase short circuits, two-phase short circuits to ground, etc. The fault type corresponding to the fault node indicates the specific fault form that has occurred at the fault node, and different fault types will cause different voltage and current change characteristics in the power system. Therefore, by determining the fault type corresponding to the fault node, it can be used to accurately analyze the fault impact and perform subsequent fault location.

[0056] The three-phase voltage before the fault of the fault node refers to the voltage values of the three phases A, B, and C at the fault node before the fault occurs.

[0057] By obtaining s historical fault events that occurred in a preset historical time period of the power system, each historical fault event includes information such as the voltage sag duration, the three-phase voltage before the fault of the access point of the sensitive load, the fault node, the fault type, and the three-phase voltage before the fault of the fault node, which can reflect the severity of the fault, the specific location and nature of the fault occurrence. Therefore, by deeply analyzing and processing these historical fault events, the characteristic laws of the power system under different fault conditions can be summarized, and thus a characteristic pattern library for fault location can be established.

[0058] S102. Determine the voltage sag amplitude characteristics according to each of the s historical fault events, and obtain s voltage sag amplitude characteristics.

[0059] In the embodiments of the present application, the voltage sag amplitude characteristic is a quantitative index of the voltage change when a fault occurs in the power system. Faults of different types and locations will cause different degrees of voltage sags at each node of the power system. By determining the voltage sag amplitude characteristic, the impact degree of the fault on the system voltage can be intuitively reflected.

[0060] In a specific embodiment, each of the s historical fault events can be specifically analyzed. For example, according to the three-phase voltage of each node after the fault in the historical fault event and the voltage sag characteristic amplitude corresponding to the node, and based on the n nodes in the power system, it can be determined that in one historical fault event, the voltage sag characteristic amplitudes corresponding to the n nodes are used to construct the voltage sag amplitude characteristic corresponding to this historical fault event, and then s voltage sag amplitude characteristics are obtained.

[0061] Optionally, the above step of determining the voltage sag amplitude characteristic according to each of the s historical fault events to obtain s voltage sag amplitude characteristics specifically includes the following steps:

[0062] A201. Determine a voltage sag amplitude calculation model according to the fault type corresponding to the fault node f in the target historical fault event; the target historical fault event is any one of the s historical fault events; the fault node f is any one of the n nodes;

[0063] A202. Determine p three-phase voltages after the fault according to the voltage sag amplitude calculation model, the three-phase voltages before the fault of the fault node f, and the three-phase voltages before the fault of the p sensitive load access points;

[0064] A203. Determine the minimum three-phase voltage of each of the p three-phase voltages after the fault to obtain p minimum three-phase voltages;

[0065] A204. Determine the target voltage sag amplitude characteristic according to the p minimum three-phase voltages; the target voltage sag amplitude characteristic is the voltage sag amplitude characteristic corresponding to the target historical fault event among the s voltage sag amplitude characteristics.

[0066] In a specific embodiment, any one of the s historical fault events is selected as the target historical fault event, and the fault node f and its corresponding fault type in the target historical fault event are determined. Different fault types will result in different voltage change laws in the power system. Therefore, an accurate voltage sag amplitude calculation model can be selected according to the fault type.

[0067] Determine the three-phase post-fault voltages of p sensitive load connection points based on the voltage sag magnitude calculation model, the pre-fault three-phase voltages of the fault node f, and the pre-fault three-phase voltages of p sensitive load connection points. That is, based on the voltage sag magnitude calculation model, input the necessary parameters such as the pre-fault three-phase voltages of the fault node f and the pre-fault three-phase voltages of p sensitive load connection points, and the three-phase post-fault voltages corresponding to each sensitive load connection point among the p sensitive load connection points can be determined, obtaining the three-phase post-fault voltages of p.

[0068] Next, determine the minimum three-phase voltage of each of the three-phase post-fault voltages of p, and p minimum three-phase voltages can be obtained. That is, find the phase voltage that is most severely affected among the three-phase post-fault voltages of each sensitive load connection point, and use this minimum value to represent the voltage sag degree of this connection point, making the voltage sag characteristics more representative. For example, the three-phase post-fault voltages of a certain sensitive load connection point m are the phase A voltage V A , the phase B voltage V B , and the phase C voltage V C . If V A is the minimum, then V A is determined as the minimum three-phase voltage corresponding to this sensitive load connection point, and the calculation formula is as follows:

[0069] V m = min(V m,A , V m,B , V m,C )

[0070] In the above formula, V m represents the minimum three-phase voltage corresponding to the sensitive load connection point m; min() represents taking the minimum value; V m,A represents the phase A voltage corresponding to the sensitive load connection point m; V m,B represents the phase B voltage corresponding to the sensitive load connection point m; V m,C represents the phase C voltage corresponding to the sensitive load connection point m.

[0071] Determine the target voltage sag magnitude characteristic based on the p minimum three-phase voltages, that is, combine the p minimum three-phase voltages according to a preset rule to form a vector or an array, and use this vector or array as the target voltage sag magnitude characteristic. Among them, the target voltage sag magnitude characteristic is the voltage sag magnitude characteristic corresponding to the target historical fault event among s voltage sag magnitude characteristics, and the preset rule can be the order of n nodes in the power system or a preset default order. The target voltage sag magnitude characteristic can be as follows:

[0072] V i = [V i,1 V i,2 … V i,n ​

[0073] In the above formula, V i represents the voltage sag amplitude characteristic corresponding to the i-th historical fault event; V i,1 represents the voltage sag amplitude corresponding to the first node in the i-th historical fault event; since the power system has n nodes, therefore, V i,n represents the voltage sag amplitude corresponding to the n-th node in the i-th historical fault event.

[0074] By specifically analyzing each of the s historical fault events and selecting a specific calculation model for different fault types, it is possible to more accurately reflect the impact of different faults on the voltage. Selecting the minimum three-phase voltage as the key feature highlights the most adverse impact of the fault on the sensitive load connection point, making this feature better represent the severity and scope of the fault, and being more distinguishable in subsequent pattern matching and fault location.

[0075] Optionally, the fault type includes any one of the following: single-phase short circuit, three-phase short circuit, two-phase short circuit, two-phase short circuit to ground; the above step of determining the voltage sag amplitude calculation model according to the fault type corresponding to the fault node f in the target historical fault event specifically includes the following steps:

[0076] B201. When the fault type is the single-phase short circuit, the voltage sag amplitude calculation model is as follows:

[0077]

[0078] In the above formula, V m,A , V m,B , V m,C respectively represent the ABC three-phase voltages of the sensitive load connection point m; respectively represent the ABC three-phase voltages before the fault of the sensitive load connection point m; respectively represent the ABC three-phase voltages before the fault of the fault node f; respectively represent the positive-sequence self-impedance, negative-sequence self-impedance, and zero-sequence self-impedance of the fault node f; respectively represent the positive-sequence mutual impedance, negative-sequence mutual impedance, and zero-sequence mutual impedance between the sensitive load connection point m and the fault node f; R f represents the resistance of the fault node f; α represents the rotation factor in the symmetrical component method; the sensitive load connection point m is any one of the p sensitive load connection points;

[0079] B202. When the fault type is the three-phase short circuit, the voltage sag amplitude calculation model is as follows:

[0080]

[0081] In the above formula, represents the three-phase voltage before the fault at the sensitive load connection point m; represents the three-phase voltage before the fault at the fault node f;

[0082] B203. When the fault type is the two-phase short circuit, the voltage sag amplitude calculation model is as follows:

[0083]

[0084] B204. When the fault type is the two-phase short circuit to ground, the voltage sag amplitude calculation model is as follows:

[0085]

[0086] Among them, there are p sensitive load connection points in the power system. Therefore, by analyzing the p sensitive load connection points in sequence, the relationship between each sensitive load connection point and the fault node is determined, and then the three-phase voltage of each sensitive load connection point after the fault is calculated according to the determined voltage sag amplitude calculation model. In the embodiments of the present application, the relationship between the sensitive load connection point m and the fault node f can be specifically analyzed, where the sensitive load connection point m is any one of the p sensitive load connection points.

[0087] Please refer to Figure 4 , Figure 4 is a schematic structural diagram of another power system provided by the embodiments of the present application. As shown in the figure, there is a fault node f on the power system line between node i and node j. Among them, p is the normalized distance between node i and the fault node f, representing the proportion of the distance from the fault node f to node i in the total length of line ij, and 1 - p represents the proportion of the distance from the fault node f to node j in the total length of line ij. The normalized distance can be used for fault location, etc.

[0088] The sensitive load connection point m is set on line ij, and relevant data on line ij can be collected through its corresponding monitoring device. R f is the fault resistance at the fault node f. When a fault occurs in the power system line, a certain resistance characteristic will be generated at the fault point, and this resistance value will affect the distribution of fault current and voltage, and can be used for analyzing the fault, such as fault type judgment and fault location calculation.

[0089] In specific embodiments, the fault type includes any one of the following: single-phase short circuit, three-phase short circuit, two-phase short circuit, two-phase short circuit to ground, which is not limited herein.

[0090] When the fault type is single-phase short circuit, the three-phase balance of the power system is broken, and the changes in the voltages of each phase are affected by the electrical parameters between the fault point and the access point of the sensitive load. Therefore, a calculation model for the voltage sag value corresponding to the single-phase short circuit is determined. This voltage sag value calculation model calculates the three-phase voltages at the access point m of the sensitive load by considering the positive-sequence self-impedance negative-sequence self-impedance zero-sequence self-impedance positive-sequence mutual impedance between the access point m of the sensitive load and the fault node f negative-sequence mutual impedance zero-sequence mutual impedance and the fault resistance R f and the rotation factor α.

[0091] It should be noted that in the symmetrical component method, the rotation factor α is a complex number. From the perspective of complex numbers, its modulus value is 1 and the argument is 120 degrees, which means rotating a phasor counterclockwise by 120 degrees around the origin in the complex plane. And is equivalent to rotating the phasor counterclockwise by 240 degrees (equivalent to rotating clockwise by 120 degrees) around the origin. The rotation factor α satisfies 1 + α + α = 0. 2

[0092] When the fault type is three-phase short circuit, the three-phase voltages drop simultaneously and the degree of drop is relatively consistent. Therefore, the calculation model for the voltage sag value determined according to this three-phase short circuit fault type is relatively simple. It mainly considers the three-phase voltages before the fault at the access point m of the sensitive load and the fault node f, the negative-sequence self-impedance of the fault node f and the fault resistance R f , as well as the negative-sequence mutual impedance between the access point m of the sensitive load and the fault node f Due to the symmetry of the three phases during three-phase short circuit, the calculation formulas for the voltage sag values of the three phases are the same.

[0093] When the fault type is two-phase short circuit, it causes asymmetric changes in the three-phase voltages. Therefore, the calculation model for the voltage sag value determined according to this two-phase short circuit fault type can, based on the characteristics of the asymmetric changes, calculate the three-phase voltages at the access point m of the sensitive load by considering the negative-sequence self-impedance of the fault node f zero-sequence self-impedance negative-sequence mutual impedance between the access point m of the sensitive load and the fault node f and zero-sequence mutual impedance and the fault resistance R f and the rotation factor α.

[0094] ​When the fault type is two-phase short circuit to ground, the three-phase balance of the power system is severely damaged. For example, in the case of a two-phase short circuit to ground between phases B and C, the short-circuit current not only flows between phases B and C, but also forms a loop with the ground through the grounding point, resulting in complex changes in the three-phase voltage and current. This fault will cause a significant drop in the voltage at each point in the power system, having a serious impact on the electrical equipment connected to the power system. Therefore, the voltage sag amplitude calculation model determined according to this two-phase short circuit to ground fault type can calculate the three-phase voltage at the access point m of the sensitive load through a more complex formula.

[0095] It can be seen that by setting corresponding voltage sag amplitude calculation models for different fault types respectively, the impact of each fault on the voltage at the access point of the sensitive load can be accurately quantified to accurately calculate the voltage sag amplitude, making the subsequent constructed voltage sag characteristic pattern library more accurate.

[0096] S103. Determine the voltage sag duration characteristics corresponding to the s historical fault events according to the preset probability distribution of voltage sag duration.

[0097] In the embodiment of the present application, the voltage sag duration corresponding to a historical fault event refers to the duration during which all n nodes in the power system jointly sag under this fault event.

[0098] In the embodiment of the present application, the preset probability distribution of voltage sag duration is a mathematical model obtained by statistically analyzing the voltage sag duration based on a large amount of historical data of the power system in advance, and is used to describe the likelihood of different voltage sag durations occurring in the system. By collecting the voltage sag duration data of fault events during the long-term operation of the power system and classifying them according to time intervals, for example, dividing the intervals at intervals of 10 milliseconds, 50 milliseconds, or 100 milliseconds, etc., and counting the number of voltage sag events occurring in each interval, the probability corresponding to each interval can be calculated, and the probability distribution of voltage sag duration can be obtained.

[0099] Of course, the preset probability distribution of voltage sag duration can also be represented by a probability distribution function, that is, the probability distribution of voltage sag duration can be a discrete probability distribution or a continuous probability distribution, and the change law of probability with duration is described by a function formula, such as exponential distribution, normal distribution, etc. The preset probability distribution of voltage sag duration reflects the inherent characteristics of the voltage sag duration in the long-term operation of the power system. That is, for different power systems, due to factors such as equipment type, network structure, and operating environment, the probability distribution of voltage sag duration will also vary. By analyzing the probability distribution, the operating conditions and potential fault risks of the power system can be deeply understood.

[0100] In specific embodiments, the voltage sag duration characteristics corresponding to s historical fault events may be determined according to a preset probability distribution of voltage sag duration. The voltage sag duration characteristics include the voltage sag duration corresponding to each historical fault event, that is, each historical fault event has a corresponding voltage sag duration characteristic value. There are many uncertain factors in the operation of the power system, resulting in a certain randomness in the voltage sag duration. Determining the duration characteristics based on the probability distribution can well adapt to this uncertainty. Therefore, determining the voltage sag duration characteristics corresponding to s historical fault events through the preset probability distribution of voltage sag duration can enhance the adaptability and reliability of the fault location method.

[0101] Optionally, the above step of determining the voltage sag duration characteristics corresponding to the s historical fault events according to the preset probability distribution of voltage sag duration specifically includes the following steps:

[0102] A301. Perform Monte Carlo simulation based on the preset probability distribution of voltage sag duration to obtain s target voltage sag durations;

[0103] A302. Determine the voltage sag duration characteristics according to the s target voltage sag durations.

[0104] Among them, Monte Carlo simulation is a statistical method based on random sampling, which can use random numbers to simulate the uncertainty in the actual process. The preset probability distribution of voltage sag duration represents the possible values of the voltage sag duration and their occurrence probabilities.

[0105] In specific embodiments, a random number generator may be used to generate random numbers that are uniformly distributed in the interval from 0 to 1, and the corresponding voltage sag duration values are determined based on the preset probability distribution of voltage sag duration and the random numbers. By repeating the sampling s times, s target voltage sag durations can be obtained, and each target voltage sag duration has its corresponding historical fault event.

[0106] It should be noted that the voltage sag duration generated by Monte Carlo simulation is used instead of directly adopting the actual value corresponding to the historical fault event. On the one hand, it is because the voltage sag duration in the power system is affected by various complex factors, such as the operating state of the system at the time of the fault, the electrical characteristics of the fault point, the action time of the protection device, etc., resulting in randomness in the voltage sag duration of each fault. Therefore, by constructing a preset probability distribution of the voltage sag duration according to these influencing factors, this randomness can be well reflected. Through Monte Carlo simulation, randomly generating the voltage sag duration based on the probability distribution can cover all possible situations more comprehensively and is more representative than only using the historical values of actual occurrences. For example, a certain type of fault has occurred 10 times in history, and the actual durations may be concentrated in a certain range, but there is still a possibility that durations outside this range may occur during the future operation of the system. Monte Carlo simulation can take into account these small-probability but still possible situations.

[0107] On the other hand, if the voltage sag duration of historical fault events is directly used, it may be affected by biases in the data acquisition process. For example, the monitoring system may malfunction, resulting in inaccurate recorded durations, or for some types of faults, due to limitations in the monitoring range, the number of recorded samples is small, resulting in statistical biases. However, Monte Carlo simulation generates data based on the probability distribution, which can reduce the influence of these biases to a certain extent, making the obtained voltage sag duration characteristics better reflect the actual fault situation.

[0108] Based on the s target voltage sag durations, the voltage sag duration characteristics can be determined, and the voltage sag duration characteristics are as follows:

[0109] T = [T1 T2 … T s T

[0110] In the above formula, T represents the voltage sag duration characteristics corresponding to s historical fault events; T1 represents the voltage sag duration corresponding to the first historical fault event; T s represents the voltage sag duration of the s-th historical fault event; T represents the transpose of the matrix.

[0111] The voltage sag duration in the power system is affected by various factors and has a certain degree of uncertainty. Monte Carlo simulation can fully consider this uncertainty, and the target voltage sag durations obtained through multiple random samplings can better reflect the actual situation, making the determined voltage sag duration characteristics more accurate and reliable.

[0112] S104. Construct a first feature pattern library according to the s voltage sag amplitude characteristics and the voltage sag duration characteristics. ​

[0113] Among them, the first feature pattern library includes s voltage sag patterns, each voltage sag pattern corresponding to a voltage sag amplitude feature and a voltage sag duration; each voltage sag pattern corresponds to a fault event.

[0114] In a specific embodiment, the voltage sag amplitude feature is the voltage sag amplitude data of each node calculated according to corresponding formulas based on factors such as fault type and location for each historical fault event, and finally s voltage sag amplitude features are obtained. The voltage sag duration feature is the duration data corresponding to each fault event obtained by methods such as Monte Carlo simulation based on a preset probability distribution, and finally the voltage sag duration feature is obtained. Therefore, by integrating the s voltage sag amplitude features and the voltage sag duration features in one-to-one correspondence, the first feature pattern library can be obtained. Among them, the first feature pattern library includes s voltage sag patterns. For example, the first voltage sag amplitude feature and the first voltage sag duration in the voltage sag duration features are combined to obtain the first first feature pattern.

[0115] Optionally, the above step of constructing the first feature pattern library according to the s voltage sag amplitude features and the voltage sag duration features specifically includes the following steps:

[0116] Construct the first feature pattern library based on a preset arrangement order according to the s voltage sag amplitude features and the voltage sag duration features, where the voltage sag amplitude feature i is as follows:

[0117] V i =[V i,1 V i,2 … V i,n

[0118] In the above formula, V i represents the voltage sag amplitude feature i, and the voltage sag amplitude feature i is any one of the s voltage sag amplitude features; V i,1 represents the voltage sag amplitude of the first node in the voltage sag amplitude feature i; V i,n represents the voltage sag amplitude of the nth node in the voltage sag amplitude feature i;

[0119] Among them, the voltage sag duration feature is as follows:

[0120] T=[T1 T2 … T s T

[0121] ​​In the above formula, T represents the voltage sag duration characteristic; T1 represents the voltage sag duration of the first historical fault event among the s historical fault events; T2 represents the voltage sag duration of the s-th historical fault event among the s historical fault events; T represents the transpose of a matrix;

[0122] Among them, the first feature pattern library is as follows:

[0123]

[0124] In the above formula, P represents the first feature pattern library; [V 1,1 , T1] represents a matrix composed of the voltage sag amplitude corresponding to the first node in the first historical fault event and the voltage sag duration T1 of the first historical fault event; [V s,n , T s represents a matrix composed of the voltage sag amplitude corresponding to the n-th node in the s-th historical fault event and the voltage sag duration T s of the s-th historical fault event.

[0125] Among them, the preset arrangement order refers to the order in which historical fault events occur. The voltage sag amplitude characteristics and voltage sag duration corresponding to historical fault events can be arranged according to the preset arrangement order.

[0126] In a specific embodiment, a first feature pattern library is constructed based on the s voltage sag amplitude characteristics and the voltage sag duration characteristic according to the preset arrangement order, that is, each voltage sag amplitude characteristic is associated with the corresponding voltage sag duration, and the first feature pattern library can be obtained.

[0127] In the first feature pattern library, the row elements of the first feature pattern library are the feature patterns corresponding to a single voltage sag event, which include the voltage sag amplitudes of n nodes in the power system during a single historical fault event and the voltage sag duration of the historical fault event.

[0128] The first feature pattern library synthesizes two key features, namely the voltage sag amplitude and the voltage sag duration, and can more comprehensively and accurately describe the characteristics of each historical fault event. Since the first feature pattern is obtained by simulating all possible contingency faults, there is a feature pattern in the first feature pattern that is most similar to the voltage sag pattern formed by the measured data. Therefore, when a voltage sag fault actually occurs, the fault characteristics monitored in real time can be matched with the patterns in the first feature pattern library to quickly and accurately locate the fault source.

[0129] S105. Obtain k monitoring data of k monitoring devices, and determine the target voltage sag pattern according to the k monitoring data.

[0130] In the embodiments of the present application, in a power system, for cost and practical application considerations, monitoring devices are not deployed at each node. Instead, k monitoring devices are installed at some key nodes (such as some sensitive load access points). The monitoring devices are distributed at different locations and can monitor the power parameters of the nodes where they are located in real time, such as voltage, current, etc.

[0131] In a specific embodiment, when a voltage sag event occurs in the power system, the k monitoring devices will record the monitoring data of their respective nodes, such as the three-phase voltage values of the nodes at the time of the fault, the voltage sag amplitude, the voltage sag duration, etc. Therefore, k monitoring data can be obtained according to the k monitoring devices.

[0132] Next, a target voltage sag pattern is constructed based on the k monitoring data. The target voltage sag pattern can be represented in the form of a matrix or a vector, and it can be as follows:

[0133]

[0134] In the above formula, M represents the target voltage sag pattern; t represents that the voltage sag duration of this voltage sag event is t; the power system includes k monitoring devices, and the node numbers where the monitoring devices are located are k1, k2,..., k k , then represents the voltage sag amplitude monitored by the k1st monitoring device.

[0135] S106. Determine a target fault event according to the target voltage sag pattern and the first feature pattern library.

[0136] In a specific embodiment, pattern matching is performed in the first feature pattern library according to the target voltage sag pattern, so as to find the historical fault event that best matches the current fault by comparing the similarity between the target voltage sag pattern and each pattern in the first feature pattern library, that is, the target fault event.

[0137] Once the target fault event is determined, the source of the current fault can be quickly located according to the relevant information of the historical fault event, such as the location where the fault occurred, the fault type, etc., which helps the maintenance personnel of the power system to take measures in time for fault repair.

[0138] Optionally, the above step of determining the target fault event according to the target voltage sag pattern and the first feature pattern library specifically includes the following steps:

[0139] A601. Determine a second feature pattern library according to the target voltage sag pattern and the first feature pattern library;

[0140] Among them, the target voltage sag mode is as follows:

[0141]

[0142] In the above formula, M represents the target voltage sag mode; represents the voltage sag amplitude monitored by the k1-th monitoring device; t represents the voltage sag duration of the current fault event;

[0143] Among them, the second characteristic mode library is as follows:

[0144]

[0145] In the above formula, represents the second characteristic mode library; represents the matrix composed of the voltage sag amplitude corresponding to the node monitored by the k1-th monitoring device in the first historical fault event and the voltage sag duration T1 of the first historical fault event; represents the matrix composed of the voltage sag amplitude corresponding to the node monitored by the k k -th monitoring device in the s-th historical fault event and the voltage sag duration T s of the s-th historical fault event;

[0146] A602. Determine the target fault event according to the target voltage sag mode and the second characteristic mode library.

[0147] In a specific embodiment, the second characteristic mode library can be determined according to the target voltage sag mode and the first characteristic mode library. Among them, the first characteristic mode library is constructed based on historical fault events and contains the voltage sag amplitudes and voltage sag durations of each node under different historical fault events. The second characteristic mode library is adjusted or screened based on the first characteristic mode library in combination with the target voltage sag mode.

[0148] In the second characteristic mode library, represents the voltage sag amplitude of the node corresponding to the k1-th monitoring device screened from the first historical fault event. In the first characteristic mode library, V 1,1 represents the voltage sag amplitude of the first node in the first historical fault event. Due to cost considerations, the number of monitoring devices in the power system can be less than the number of nodes. Therefore, assuming that the third node in the power system is a key node, the monitoring device numbered k1 can be set on the third node to monitor the third node. Then, the value of in the second characteristic mode library is the value of V 1,3 in the first characteristic mode library.

[0149] Next, determine the target fault event according to the target voltage sag mode and the second feature pattern library. Specifically, after obtaining the second feature pattern library, compare the target voltage sag mode with each second feature pattern in the second feature pattern library. Specifically, methods such as the Euclidean distance method and the cosine similarity method can be used to calculate the similarity index between the target voltage sag mode and each mode in the second feature pattern library, and find the mode with the highest similarity. The historical fault event corresponding to the mode with the highest similarity is the target fault event. After determining the target fault event, information corresponding to the target fault event, such as the location where the fault occurred and the fault type, can be determined to quickly locate the source of the current fault.

[0150] Please refer to Figure 5 , Figure 5 FIG. Figure 5 is a schematic diagram for constructing a second feature pattern library provided by an embodiment of the present application. As shown in the figure, there are 10 nodes in the power system, and four monitoring devices numbered k1, k2, k3, and k4 are respectively set on nodes 2, 4, 7, and 8. In the process of constructing the first feature pattern library, s historical fault events are obtained, and the voltage sag amplitudes of all nodes in each historical fault event are determined, and s voltage sag amplitude features are constructed. Each historical fault event corresponds to a voltage sag amplitude feature, and each historical fault event corresponds to a voltage sag duration.

[0151] During actual monitoring, only key nodes are monitored by monitoring devices. For example, nodes 2, 4, 7, and 8 are respectively monitored by the four monitoring devices k1, k2, k3, and k4. Then, in actual application, the second feature pattern library is constructed by screening out the voltage sag amplitudes corresponding to nodes 2, 4, 7, and 8 from the first feature pattern library. In the second feature pattern library, k1 in fault event S1 represents the voltage sag amplitude corresponding to node 2 in historical fault event S1 in the first feature pattern library.

[0152] Optionally, the above step of determining the target fault event according to the target voltage sag mode and the second feature pattern library specifically includes the following steps:

[0153] B601. Determine s mode matching error indicators according to the target voltage sag mode and the second feature pattern library;

[0154] Among them, the calculation formula of the mode matching error indicator is as follows:

[0155]

[0156] In the above formula, e1 represents the pattern matching error index between the target voltage sag mode and the first second feature pattern in the second feature pattern library; t represents the voltage sag duration of the current fault event; T1 represents the voltage sag duration of the first second feature pattern in the second feature pattern library; k represents that there are k monitoring devices; represents the voltage sag amplitude monitored by the i-th monitoring device; represents the voltage sag amplitude of the k i th node in the first voltage sag amplitude feature;

[0157] B602. Construct a pattern matching error matrix according to the s pattern matching error indexes;

[0158] Among them, the pattern matching error matrix is shown as follows:

[0159] E = [e1 e2 … e s T

[0160] In the above formula, E represents the pattern matching error matrix; e1 represents the first pattern matching error index; e s represents the s-th pattern matching error index;

[0161] B603. Search for the column number corresponding to the minimum row element in the pattern matching error matrix according to the following formula to obtain the target column number;

[0162] B604. Determine the target feature pattern according to the target column number and the second feature pattern library;

[0163] B605. Determine the target fault event according to the target feature pattern.

[0164] Among them, the pattern matching error index is used to measure the difference degree between the target voltage sag mode and each second feature pattern in the second feature pattern library. The smaller the difference, the more similar the two patterns are.

[0165] In a specific embodiment, s pattern matching error indexes can be determined according to the target voltage sag mode and the second feature pattern library. For example, for the first second feature pattern, the square of the difference between the voltage sag duration of the current fault event and the voltage sag duration of the first second feature pattern can be determined first, and then the squares of the differences between all the actual voltage sag amplitudes determined for k monitoring devices and the corresponding voltage sag amplitudes in the first second feature pattern are summed, and then the pattern matching error index between the target voltage sag mode and the first second feature pattern in the second feature pattern library is determined. Based on this, the pattern matching error indexes between the target voltage sag mode and each second feature pattern in the second feature pattern library can be determined, and s pattern matching error indexes are obtained.​

[0166] Construct a pattern matching error matrix based on s pattern matching error metrics. In the pattern matching error matrix, the pattern corresponding to the smallest error metric is the most similar to the target voltage sag pattern. Therefore, by searching for the column number where the smallest element in the matrix is located, the position of the most matching pattern in the second feature pattern library can be found. The formula is as follows:

[0167] R = argmin(E)

[0168] In the above formula, R represents the target column number; argmin() represents the minimum value search operator, which is used to return the column number corresponding to the minimum row element in the pattern matching error matrix E.

[0169] After obtaining the target column number, it can be determined that the second feature pattern corresponding to the target column number in the second feature pattern library is the most similar to the target feature pattern, that is, the target feature pattern is obtained. And the target feature pattern corresponds to a historical fault event, and this historical fault event is the target fault event. By determining the target fault event, its relevant information such as the location where the fault occurred and the fault type can be obtained.

[0170] Please refer to Figure 6 , Figure 6 which is an application scenario diagram of a pattern matching method provided by an embodiment of the present application. As shown in the figure, in the power system, corresponding data of nodes 2, 4, 7, and 8 are collected through four monitoring devices numbered k1, k2, k3, and k4 respectively, and 4 voltage sag amplitudes are obtained, and a target voltage sag pattern is constructed. Then, the target voltage sag pattern is compared with each second feature pattern in the second feature pattern library. Finally, it can be determined that the pattern matching error metric corresponding to the historical fault event S2 is the smallest. Therefore, the historical fault event S2 can be used as the target fault event.

[0171] By quantifying the pattern matching error and using matrix operations and search methods, it is possible to accurately and efficiently find the pattern in the second feature pattern library that best matches the target voltage sag pattern, and then determine the target fault event to accurately locate the voltage sag source.

[0172] In summary, by implementing the embodiments of the present application, s historical fault events that occurred in a preset historical time period of the power system are obtained; voltage sag amplitude value characteristics are determined according to each of the s historical fault events, and s voltage sag amplitude value characteristics are obtained; according to the preset probability distribution of voltage sag duration, the voltage sag duration characteristics corresponding to the s historical fault events are determined; a first feature pattern library is constructed according to the s voltage sag amplitude value characteristics and the voltage sag duration characteristics; k monitoring data of k monitoring devices are obtained, and a target voltage sag pattern is determined according to the k monitoring data; a target fault event is determined according to the target voltage sag pattern and the first feature pattern library. It can be seen that by constructing a feature pattern library with historical fault events and through pattern matching, accurate voltage sag source location can be performed.

[0173] Please refer to Figure 7 , Figure 7 FIG. is a schematic structural diagram of a voltage sag source location device based on pattern matching provided by an embodiment of the present application. The voltage sag source location device 700 based on pattern matching is applied to a control device of a power system. The power system further includes: n nodes, p sensitive load access points, and k monitoring devices, where n, p, and k are all positive integers; the voltage sag source location device 700 based on pattern matching includes: a first data acquisition module 701, a first feature determination module 702, a second feature determination module 703, a feature pattern library construction module 704, a second data acquisition module 705, and a fault location module 706, where

[0174] The first data acquisition module 701 is configured to obtain s historical fault events that occurred in a preset historical time period of the power system; the historical fault events include: voltage sag duration, pre-fault three-phase voltages of the p sensitive load access points, fault nodes, fault types corresponding to the fault nodes, and pre-fault three-phase voltages of the fault nodes; s is a positive integer;

[0175] The first feature determination module 702 is configured to determine voltage sag amplitude value characteristics according to each of the s historical fault events, and obtain s voltage sag amplitude value characteristics;

[0176] The second feature determination module 703 is configured to determine voltage sag duration characteristics corresponding to the s historical fault events according to a preset probability distribution of voltage sag duration;

[0177] The feature pattern library construction module 704 is used to construct a first feature pattern library according to the s voltage sag amplitude features and the voltage sag duration features; the first feature pattern library includes s voltage sag patterns, each voltage sag pattern corresponding to a voltage sag amplitude feature and a voltage sag duration; each voltage sag pattern corresponds to a fault event.

[0178] The second data acquisition module 705 is used to obtain k monitoring data of the k monitoring devices and determine a target voltage sag pattern according to the k monitoring data.

[0179] The fault location module 706 is used to determine a target fault event according to the target voltage sag pattern and the first feature pattern library.

[0180] Optionally, in terms of determining the voltage sag amplitude features according to each of the s historical fault events to obtain s voltage sag amplitude features, the first feature determination module 702 is further specifically used for:

[0181] Determine a voltage sag amplitude calculation model according to the fault type corresponding to the fault node f in the target historical fault event; the target historical fault event is any one of the s historical fault events; the fault node f is any one of the n nodes.

[0182] Determine p post-fault three-phase voltages according to the voltage sag amplitude calculation model, the pre-fault three-phase voltages of the fault node f, and the pre-fault three-phase voltages of the p sensitive load connection points.

[0183] Determine the minimum three-phase voltage of each of the p post-fault three-phase voltages to obtain p minimum three-phase voltages.

[0184] Determine a target voltage sag amplitude feature according to the p minimum three-phase voltages; the target voltage sag amplitude feature is the voltage sag amplitude feature corresponding to the target historical fault event among the s voltage sag amplitude features.

[0185] Optionally, the fault type includes any one of the following: single-phase short circuit, three-phase short circuit, two-phase short circuit, two-phase short circuit to ground; in terms of determining the voltage sag amplitude calculation model according to the fault type corresponding to the fault node f in the target historical fault event, the first feature determination module 702 is further specifically used for:

[0186] When the fault type is the single-phase short circuit, the voltage sag amplitude calculation model is as follows:

[0187]

[0188] In the above formula, V m,A , V m,B , V m,C respectively represent the ABC - phase voltages at the sensitive load connection point m; respectively represent the ABC - phase voltages before the fault at the sensitive load connection point m; respectively represent the ABC - phase voltages before the fault at the fault node f; respectively represent the positive - sequence self - impedance, negative - sequence self - impedance, and zero - sequence self - impedance of the fault node f; respectively represent the positive - sequence mutual impedance, negative - sequence mutual impedance, and zero - sequence mutual impedance between the sensitive load connection point m and the fault node f; R f represents the resistance of the fault node f; α represents the rotation factor in the symmetrical component method; the sensitive load connection point m is any one of the p sensitive load connection points;

[0189] When the fault type is three - phase short - circuit, the voltage sag amplitude calculation model is as follows:

[0190]

[0191] In the above formula, represents the three - phase voltage before the fault at the sensitive load connection point m; represents the three - phase voltage before the fault at the fault node f;

[0192] When the fault type is two - phase short - circuit, the voltage sag amplitude calculation model is as follows:

[0193]

[0194] When the fault type is two - phase short - circuit to ground, the voltage sag amplitude calculation model is as follows:

[0195]

[0196] Optionally, in terms of determining the voltage sag duration characteristics corresponding to the s historical fault events according to the preset voltage sag duration probability distribution, the second feature determination module 703 is further specifically configured to:

[0197] Perform Monte Carlo simulation based on the preset voltage sag duration probability distribution to obtain s target voltage sag durations;

[0198] Determine the voltage sag duration characteristics according to the s target voltage sag durations.

[0199] Optionally, in constructing the first feature pattern library according to the s voltage sag amplitude characteristics and the voltage sag duration characteristics, the feature pattern library construction module 704 is further specifically configured to:

[0200] Construct a first feature pattern library based on the s voltage sag amplitude characteristics and the voltage sag duration characteristics according to a preset arrangement order, where the voltage sag amplitude characteristic i is as follows:

[0201] V i =[V i,1 V i,2 …V i,n

[0202] In the above formula, V i represents the voltage sag amplitude characteristic i, and the voltage sag amplitude characteristic i is any one of the s voltage sag amplitude characteristics; V i,1 represents the voltage sag amplitude of the first node in the voltage sag amplitude characteristic i; V i,n represents the voltage sag amplitude of the nth node in the voltage sag amplitude characteristic i;

[0203] Among them, the voltage sag duration characteristic is as follows:

[0204] T=[T1T2…T s T

[0205] In the above formula, T represents the voltage sag duration characteristic; T1 represents the voltage sag duration of the first historical fault event among the s historical fault events; T2 represents the voltage sag duration of the s historical fault event among the s historical fault events; [] T represents the transpose of the matrix;

[0206] Among them, the first feature pattern library is as follows:

[0207]

[0208] In the above formula, P represents the first feature pattern library; [V 1,1 ,T1] represents the matrix formed by the voltage sag amplitude corresponding to the first node in the first historical fault event and the voltage sag duration T1 of the first historical fault event; [V s,n ,T s represents the matrix formed by the voltage sag amplitude corresponding to the nth node in the s historical fault event and the voltage sag duration T s of the s historical fault event.

[0209] ​​Optionally, in determining the target fault event according to the target voltage sag mode and the first feature mode library, the fault location module 706 is further specifically configured to:

[0210] Determine a second feature mode library according to the target voltage sag mode and the first feature mode library;

[0211] Wherein, the target voltage sag mode is as follows:

[0212]

[0213] In the above formula, M represents the target voltage sag mode; represents the voltage sag amplitude monitored by the k1-th monitoring device; t represents the voltage sag duration of the current fault event;

[0214] Wherein, the second feature mode library is as follows:

[0215]

[0216] In the above formula, represents the second feature mode library; represents the matrix composed of the voltage sag amplitude corresponding to the node monitored by the k1-th monitoring device in the first historical fault event and the voltage sag duration T1 of the first historical fault event; represents the matrix composed of the voltage sag amplitude corresponding to the node monitored by the k k -th monitoring device in the s-th historical fault event and the voltage sag duration T s of the s-th historical fault event;

[0217] Determine the target fault event according to the target voltage sag mode and the second feature mode library.

[0218] Optionally, in determining the target fault event according to the target voltage sag mode and the second feature mode library, the fault location module 706 is further specifically configured to:

[0219] Determine s pattern matching error metrics according to the target voltage sag mode and the second feature mode library;

[0220] Wherein, the calculation formula of the pattern matching error metric is as follows:

[0221]

[0222] In the above formula, e1 represents the pattern matching error index between the target voltage sag mode and the first second feature mode in the second feature mode library; t represents the voltage sag duration of the current fault event; T1 represents the voltage sag duration of the first second feature mode in the second feature mode library; k represents that there are k monitoring devices; represents the voltage sag amplitude monitored by the i-th monitoring device; represents the voltage sag amplitude of the k-th node in the first voltage sag amplitude feature; i

[0223] Construct a pattern matching error matrix according to the s pattern matching error indexes;

[0224] Among them, the pattern matching error matrix is shown as follows:

[0225] E = [e1 e2 … e s T

[0226] In the above formula, E represents the pattern matching error matrix; e1 represents the first pattern matching error index; e s represents the s-th pattern matching error index;

[0227] Search for the column number corresponding to the minimum row element in the pattern matching error matrix according to the following formula to obtain the target column number;

[0228] Determine the target feature pattern according to the target column number and the second feature mode library;

[0229] Determine the target fault event according to the target feature pattern.

[0230] The voltage sag source location device 700 based on pattern matching described in this application can obtain s historical fault events that occurred in the preset historical time period of the power system; determine the voltage sag amplitude characteristics according to each historical fault event in the s historical fault events to obtain s voltage sag amplitude characteristics; determine the voltage sag duration characteristics corresponding to the s historical fault events according to the preset voltage sag duration probability distribution; construct a first feature mode library according to the s voltage sag amplitude characteristics and the voltage sag duration characteristics; obtain k monitoring data of k monitoring devices, and determine the target voltage sag mode according to the k monitoring data; determine the target fault event according to the target voltage sag mode and the first feature mode library. It can be seen that by constructing a feature mode library through historical fault events and using the method of pattern matching, accurate voltage sag source location can be performed.

[0231] Please refer to Figure 8 , Figure 8 ​​It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device may include a processor, a memory, a communication interface, and one or more programs. The processor, the memory, and the communication interface may be interconnected through a bus. The above one or more programs are stored in the above memory and are configured to be executed by the above processor. In the embodiment of the present application, the above program includes instructions for performing the following steps:

[0232] Obtain s historical fault events that occurred in a preset historical time period of the power system. The historical fault events include: voltage sag duration, pre-fault three-phase voltages of the p sensitive load access points, fault nodes among the n nodes, fault types corresponding to the fault nodes, and pre-fault three-phase voltages of the fault nodes. s is a positive integer.

[0233] Determine voltage sag amplitude features according to each of the s historical fault events to obtain s voltage sag amplitude features.

[0234] Determine voltage sag duration features corresponding to the s historical fault events according to a preset voltage sag duration probability distribution.

[0235] Construct a first feature pattern library according to the s voltage sag amplitude features and the voltage sag duration features. The first feature pattern library includes s voltage sag patterns, and each voltage sag pattern corresponds to a voltage sag amplitude feature and a voltage sag duration. Each voltage sag pattern corresponds to a fault event.

[0236] Obtain k monitoring data of k monitoring devices, and determine a target voltage sag pattern according to the k monitoring data.

[0237] Determine a target fault event according to the target voltage sag pattern and the first feature pattern library.

[0238] The electronic device described in the present application can obtain s historical fault events that occurred in a preset historical time period of the power system; determine voltage sag amplitude features according to each of the s historical fault events to obtain s voltage sag amplitude features; determine voltage sag duration features corresponding to the s historical fault events according to a preset voltage sag duration probability distribution; construct a first feature pattern library according to the s voltage sag amplitude features and the voltage sag duration features; obtain k monitoring data of k monitoring devices, and determine a target voltage sag pattern according to the k monitoring data; determine a target fault event according to the target voltage sag pattern and the first feature pattern library. It can be seen that by constructing a feature pattern library through historical fault events and through pattern matching, accurate voltage sag source location can be performed.

[0239] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute some or all of the steps of any one of the methods described in the foregoing method embodiments, and the foregoing computer includes an electronic device.

[0240] An embodiment of the present application further provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute some or all of the steps of any one of the methods described in the foregoing method embodiments. The computer program product may be a software installation package, and the foregoing computer includes an electronic device.

[0241] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the foregoing embodiments can be completed by a computer program instructing relevant hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the foregoing method embodiments. The foregoing storage medium includes: ROM or random access memory RAM, magnetic disk, or optical disk and other media that can store program codes.

[0242] The steps of the methods or algorithms described in the embodiments of the present application can be implemented in a hardware manner or by a processor executing software instructions. The software instructions can be composed of corresponding software modules. The software modules can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, removable hard disk, compact disc read-only memory (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. Additionally, the ASIC can be located in a terminal device or a management device. Of course, the processor and the storage medium can also exist as discrete components in the terminal device or the management device.

[0243] Those skilled in the art should be able to realize that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a digital video disc (DVD)), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0244] Each device and product described in the above embodiments includes various modules / units, which can be software modules / units, hardware modules / units, or can be partially software modules / units and partially hardware modules / units. For example, for each device and product applied to or integrated into a chip, each module / unit it includes can be implemented in the form of hardware such as circuits, or at least some of the modules / units can be implemented in the form of software programs that run on a processor integrated inside the chip, and the remaining (if any) part of the modules / units can be implemented in the form of hardware such as circuits; for each device and product applied to or integrated into a chip module, each module / unit it includes can be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component (such as a chip, a circuit module, etc.) or different components of the chip module, or at least some of the modules / units can be implemented in the form of software programs that run on a processor integrated inside the chip module, and the remaining (if any) part of the modules / units can be implemented in the form of hardware such as circuits; for each device and product applied to or integrated into a terminal device, each module / unit it includes can be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component (such as a chip, a circuit module, etc.) or different components inside the terminal device, or at least some of the modules / units can be implemented in the form of software programs that run on a processor integrated inside the terminal device, and the remaining (if any) part of the modules / units can be implemented in the form of hardware such as circuits.

[0245] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the embodiments of the present application. It should be understood that the above description is only the specific embodiments of the embodiments of the present application and is not used to limit the protection scope of the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application shall be included in the protection scope of the embodiments of the present application.

Claims

1. A method for locating voltage sag sources based on pattern matching, characterized in that A control device applied to a power system, the power system further comprising: n nodes, p sensitive load access points, and k monitoring devices, where n, p, and k are all positive integers; the method comprising: Obtaining s historical fault events that occurred in a preset historical time period of the power system; the historical fault events include: voltage sag duration, pre-fault three-phase voltages of the p sensitive load access points, fault nodes among the n nodes, fault types corresponding to the fault nodes, and pre-fault three-phase voltages of the fault nodes; s is a positive integer; Determining voltage sag amplitude features according to each of the s historical fault events, obtaining s voltage sag amplitude features; Determining voltage sag duration features corresponding to the s historical fault events according to a preset voltage sag duration probability distribution; Constructing a first feature pattern library according to the s voltage sag amplitude features and the voltage sag duration features; the first feature pattern library includes s voltage sag patterns, each voltage sag pattern corresponding to a voltage sag amplitude feature and a voltage sag duration; each voltage sag pattern corresponds to a fault event; Obtaining k monitoring data of the k monitoring devices and determining a target voltage sag pattern according to the k monitoring data; Determining a target fault event according to the target voltage sag pattern and the first feature pattern library.

2. The method according to claim 1, characterized in that The determining voltage sag amplitude features according to each of the s historical fault events, obtaining s voltage sag amplitude features, includes: Determining a voltage sag amplitude calculation model according to the fault type corresponding to the fault node f in the target historical fault event; the target historical fault event is any one of the s historical fault events; the fault node f is any one of the n nodes; Determining p post-fault three-phase voltages according to the voltage sag amplitude calculation model, the pre-fault three-phase voltage of the fault node f, and the pre-fault three-phase voltages of the p sensitive load access points; Determining the minimum three-phase voltage of each of the p post-fault three-phase voltages to obtain p minimum three-phase voltages; Determining a target voltage sag amplitude feature according to the p minimum three-phase voltages; the target voltage sag amplitude feature is the voltage sag amplitude feature corresponding to the target historical fault event among the s voltage sag amplitude features.

3. The method according to claim 2, wherein The fault types include any one of the following: single-phase short circuit, three-phase short circuit, two-phase short circuit, two-phase short circuit to ground; the determining a voltage sag amplitude calculation model according to the fault type corresponding to the fault node f in the target historical fault event includes: When the fault type is the single-phase short circuit, the voltage sag amplitude calculation model is as follows: In the above formula, V m,A , V m,B , V m,C respectively represent the ABC three-phase voltages at the sensitive load connection point m; respectively represent the ABC three-phase voltages before the fault at the sensitive load connection point m; respectively represent the ABC three-phase voltages before the fault at the fault node f; respectively represent the positive-sequence self-impedance, negative-sequence self-impedance, and zero-sequence self-impedance of the fault node f; respectively represent the positive-sequence mutual impedance, negative-sequence mutual impedance, and zero-sequence mutual impedance between the sensitive load connection point m and the fault node f; R f represents the resistance of the fault node f; α represents the rotation factor in the symmetrical component method; the sensitive load connection point m is any one of the p sensitive load connection points; When the fault type is the three-phase short circuit, the voltage sag amplitude calculation model is as follows: In the above formula, represents the three-phase voltage before the failure of the sensitive load connection point m; represents the three-phase voltage before the failure of the fault node f; When the fault type is the two-phase short circuit, the voltage sag amplitude calculation model is as follows: When the fault type is the two-phase short circuit to ground, the voltage sag amplitude calculation model is as follows:

4. The method according to claim 1, wherein Determining the voltage sag duration characteristics corresponding to the s historical fault events according to the preset probability distribution of voltage sag duration includes: Performing Monte Carlo simulation based on the preset probability distribution of voltage sag duration to obtain s target voltage sag durations; Determining the voltage sag duration characteristics according to the s target voltage sag durations.

5. The method according to any one of claims 1-4, characterized in that Constructing a first feature pattern library according to the s voltage sag amplitude characteristics and the voltage sag duration characteristics includes: Constructing a first feature pattern library based on the preset arrangement order according to the s voltage sag amplitude characteristics and the voltage sag duration characteristics, where the voltage sag amplitude characteristic i is as follows: V i = [V i,1 V i,2 …V i,n ​ In the above formula, V i represents the voltage sag amplitude characteristic i, and the voltage sag amplitude characteristic i is any one of the s voltage sag amplitude characteristics; V i,1 represents the voltage sag amplitude of the first node in the voltage sag amplitude characteristic i; V i,n represents the voltage sag amplitude of the nth node in the voltage sag amplitude characteristic i; Wherein, the voltage sag duration characteristic is as follows: T = [T1 T2…T s T ​ In the above formula, T represents the voltage sag duration feature; T1 represents the voltage sag duration of the first historical fault event among the s historical fault events; T2 represents the voltage sag duration of the s-th historical fault event among the s historical fault events; T represents the transpose of a matrix; Wherein, the first feature pattern library is as follows: In the above formula, P represents the first characteristic pattern library; [V 1,1 , T1] represents a matrix composed of the voltage sag amplitude corresponding to the first node in the first historical fault event and the voltage sag duration T1 of the first historical fault event; [V s,n , T s represents a matrix composed of the voltage sag amplitude corresponding to the nth node in the sth historical fault event and the voltage sag duration T s of the sth historical fault event.

6. The method according to claim 5, wherein Determining the target fault event according to the target voltage sag pattern and the first feature pattern library includes: Determining a second feature pattern library according to the target voltage sag pattern and the first feature pattern library; Wherein, the target voltage sag pattern is as follows: In the above formula, M represents the target voltage sag mode; represents the voltage sag amplitude monitored by the k1-th monitoring device; t represents the voltage sag duration of the current fault event; Wherein, the second feature pattern library is as follows: In the above formula, represents the second characteristic pattern library; represents a matrix composed of the voltage sag amplitude corresponding to the node monitored by the k1-th monitoring device in the first historical fault event and the voltage sag duration T1 of the first historical fault event; represents the voltage sag amplitude corresponding to the node monitored by the k k -th monitoring device in the s-th historical fault event and the voltage sag duration T s of the s-th historical fault event; Determining the target fault event according to the target voltage sag pattern and the second feature pattern library.

7. The method according to claim 6, wherein Determining the target fault event according to the target voltage sag pattern and the second feature pattern library includes: Determining s pattern matching error metrics according to the target voltage sag pattern and the second feature pattern library; Wherein, the calculation formula of the pattern matching error metric is as follows: In the above formula, e1 represents the pattern matching error index between the target voltage sag mode and the first second characteristic mode in the second characteristic mode library; t represents the voltage sag duration of the current fault event; T1 represents the voltage sag duration of the first second characteristic mode in the second characteristic mode library; k represents that there are k monitoring devices; represents the voltage sag amplitude monitored by the i-th monitoring device; represents the voltage sag amplitude of the k-th node in the first voltage sag amplitude characteristic; i node; Constructing a pattern matching error matrix according to the s pattern matching error metrics; Wherein, the pattern matching error matrix is as follows: E = [e1 e2 … e s T ​ In the above formula, E represents the pattern matching error matrix; e1 represents the first pattern matching error index; e s represents the s-th pattern matching error index; Searching for the column number corresponding to the minimum row element in the pattern matching error matrix by the following formula to obtain the target column number; Determining the target feature pattern according to the target column number and the second feature pattern library; Determining the target fault event according to the target feature pattern.

8. A voltage sag source location device based on pattern matching, characterized in that, A control device applied to a power system, the power system further includes: n nodes, p sensitive load access points, k monitoring devices, where n, p, and k are all positive integers; the voltage sag source location device based on pattern matching includes: a first data acquisition module, a first feature determination module, a second feature determination module, a feature pattern library construction module, a second data acquisition module, and a fault location module, where The first data acquisition module is used to obtain s historical fault events that occurred in the preset historical time period of the power system; the historical fault events include: voltage sag duration, the pre-fault three-phase voltages of the p sensitive load access points, the fault node, the fault type corresponding to the fault node, and the pre-fault three-phase voltages of the fault node; s is a positive integer; The first feature determination module is used to determine the voltage sag amplitude characteristics according to each of the s historical fault events to obtain s voltage sag amplitude characteristics; The second feature determination module is used to determine the voltage sag duration characteristics corresponding to the s historical fault events according to the preset probability distribution of voltage sag duration; The feature pattern library construction module is used to construct a first feature pattern library according to the s voltage sag amplitude features and the voltage sag duration features; the first feature pattern library includes s voltage sag patterns, and each voltage sag pattern corresponds to a voltage sag amplitude feature and a voltage sag duration; each voltage sag pattern corresponds to a fault event; The second data acquisition module is used to obtain the k monitoring data of the k monitoring devices and determine the target voltage sag pattern according to the k monitoring data; The fault location module is used to determine the target fault event according to the target voltage sag pattern and the first feature pattern library.

9. An electronic device, characterized in that, Comprising: A processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing the steps in the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program includes program instructions, and when the program instructions are executed by the processor, the processor is caused to execute the method according to any one of claims 1-7.

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