Distributed fault positioning method and device for closed-loop power distribution network, equipment and medium

Abstract

The invention discloses a distributed fault positioning method and device for a closed-loop power distribution network, equipment and a medium, which are used for solving the technical problems of low fault positioning accuracy and low sensitivity of the closed-loop power distribution network. Comprising the following steps: determining a detection terminal which firstly detects current abrupt change as a reference terminal, and determining a moment when the current abrupt change is detected as a fault moment; acquiring first current waveform data of a cyclic wave where the fault moment is located from a reference terminal; extracting a time domain current sampling sequence as reference waveform data; with the fault moment as a reference, second current waveform data of a preset number of cyclic waves are obtained from adjacent detection terminals on all sides of the reference terminal; extracting a target section with the highest waveform similarity with the reference waveform data from the second current waveform data of the adjacent detection terminal; extracting a time domain current sampling sequence of the target section as reference waveform data; and carrying out fault positioning according to the waveform similarity between the reference waveform data and each piece of reference waveform data.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution networks, and in particular to a distributed fault location method, device, equipment and medium for a closed-loop power distribution network. Background Art

[0002] With the rapid development of renewable energy, a large number of new energy sources such as solar energy and wind energy have been connected to the distribution network, which has gradually evolved the structure of the distribution network from the traditional single power source radial structure to a complex structure with multiple power sources. This structural change has greatly improved the power supply reliability, equipment utilization and distribution network operation rate, but also posed new challenges to the original protection, reclosing and other functions of the distribution network.

[0003] Traditional distribution network control methods are mainly divided into centralized control and local distributed control. The centralized control type relies on the master station to analyze the uploaded line data, determine the fault section and issue control commands, but this method has problems with communication delay and high processing pressure on the master station. The local distributed type directly collects and analyzes data through each terminal unit, and completes the fault location and rapid recovery by setting different export protection delays for the terminals on the line. Although the line action time is shortened, it may still face the problem of protection misoperation or refusal in complex networks with multiple power sources. In particular, with the access of distributed power sources, the network structure and current direction of the distribution network have changed significantly, and the original distributed protection mechanism often fails. Intelligent distributed protection technology came into being. It is based on advanced communication and information technology, and locates and isolates faults through network topology, effectively reducing the fault isolation time and the number of protection actions, and improving power supply reliability.

[0004] However, many existing intelligent distributed methods still treat distributed generation as loads and do not fully consider their impact on fault current. Figure 1 This is a simplified diagram of a closed-loop distribution network with distributed power sources, which introduces the fault handling process of intelligent distributed power. Among them, CB1 and CB2 are outgoing circuit breakers of substations; S1, S2, S3, S4, S5, and S6 are all circuit breakers; S1, S2, S3, S4, S5, and S6 are all in the closed state during normal operation; point F is the fault point. When a fault occurs at F, the terminal unit at S1 detects the fault and exchanges information through communication between terminal units. It is learned that S2 and S3 have not detected the fault, so it is judged that the fault exists between S1 and S2, then S1 and S2 are opened to isolate the fault, and then CB2 is closed to transfer power to S3, S4, S5, and S6.

[0005] In actual circuit operation, distributed power sources not only provide current, but may also participate in load power supply. When a fault occurs, the impact of distributed power sources on the fault current is complex and changeable. It may increase the fault current and cause false operation, or it may attenuate the fault current and cause refusal to operate. This uncertainty poses a severe challenge to the sensitivity and reliability of intelligent distributed protection. In addition, the current intelligent distributed system mainly uses the transmission of fault current mutation signals between terminals to perform real-time line status analysis. Figure 2 As shown. When a fault occurs at F1, the distributed power source DG1 has an increasing effect on the fault current flowing through S3, which is easy to cause false operation, and has an attenuation effect on the fault current flowing through S2, which is easy to cause S2 to refuse to operate; when a fault occurs at F, the reverse current provided by DG1 is easy to cause S2 to malfunction. The current intelligent distributed system uses the transmission of fault current mutation signals between terminals to perform real-time line status analysis, and does not fully consider the sensitivity of fault analysis based on current mutation signals, as well as the impact of distributed power sources on the distribution network. Especially after the distributed power source is connected, the complex changes in the fault current make it difficult for traditional current mutation detection methods to accurately determine the fault location and type.

[0006] In addition, in the existing intelligent distributed methods that draw on the idea of ​​differential protection, most of them only use an error function to solve the signal synchronization problem, but do not fundamentally solve the signal synchronization problem. Summary of the invention

[0007] The present invention provides a distributed fault location method, device, equipment and medium for a closed-loop distribution network, which are used to solve the technical problems of low accuracy and low sensitivity in closed-loop distribution network fault location.

[0008] The present invention provides a distributed fault location method for a closed-loop distribution network, wherein the closed-loop distribution network is provided with a plurality of circuit breakers; each of the circuit breakers is provided with a detection terminal; the method comprises:

[0009] The detection terminal that first detects the current mutation is determined as the reference terminal, and the moment when the current mutation is detected is determined as the fault moment;

[0010] Acquire the first current waveform data of the cycle at the time of the fault from the reference terminal;

[0011] Extracting a time-domain current sampling sequence from the first current waveform data as reference waveform data;

[0012] Taking the fault moment as a reference, obtaining a preset number of cycles of second current waveform data from adjacent detection terminals on each side of the reference terminal;

[0013] Extracting a target section having the highest waveform similarity to the reference waveform data from the second current waveform data of the adjacent detection terminal;

[0014] Extracting a time-domain current sampling sequence of the target section as reference waveform data;

[0015] Fault location is performed based on the waveform similarity between the benchmark waveform data and each of the reference waveform data.

[0016] Optionally, the step of extracting a time-domain current sampling sequence from the first current waveform data as reference waveform data includes:

[0017] A time domain current sampling sequence is extracted from the first current waveform data as reference waveform data by presetting the number of sampling points.

[0018] Optionally, the step of acquiring a preset number of cycles of second current waveform data from adjacent detection terminals on each side of the reference terminal based on the fault moment comprises:

[0019] Determining whether current flow directions of adjacent detection terminals on the same side of a single side of the reference terminal are the same;

[0020] If they are the same, starting from n cycles before the fault moment, the third current waveform data of m cycles of each adjacent detection terminal on the same side are respectively obtained, wherein m>n;

[0021] The third current waveform data on the same side is superimposed to obtain the second current waveform data.

[0022] Optionally, the step of extracting the target section having the highest waveform similarity to the reference waveform data from the second current waveform data of the adjacent detection terminal comprises:

[0023] Dividing the second current waveform data into a plurality of sections through a sliding window with a preset number of sampling points and a preset number of sliding points;

[0024] sequentially calculating the waveform similarity between the waveform data of each segment and the reference waveform data;

[0025] The segment with the highest waveform similarity is determined as the target segment.

[0026] Optionally, the step of locating the fault according to the waveform similarity between the benchmark waveform data and each of the reference waveform data comprises:

[0027] A section between the detection terminal and the reference terminal corresponding to the reference waveform data whose waveform similarity with the reference waveform data is less than a preset similarity threshold is determined as a fault area.

[0028] Optionally, after the step of determining as a fault area the section between the detection terminal and the reference terminal corresponding to the reference waveform data whose waveform similarity with the reference waveform data is less than a preset similarity threshold, the method further includes:

[0029] Disconnect the circuit breaker corresponding to the fault area.

[0030] The present invention also provides a distributed fault location device for a closed-loop distribution network, wherein the closed-loop distribution network is provided with a plurality of circuit breakers; each of the circuit breakers is provided with a detection terminal; the device comprises:

[0031] A reference terminal and fault moment determination module, used to determine the detection terminal that first detects the current mutation as the reference terminal, and determine the moment when the current mutation is detected as the fault moment;

[0032] A first current waveform data acquisition module, used to acquire first current waveform data of the cycle at the time of the fault from the reference terminal;

[0033] A reference waveform data extraction module, used to extract a time domain current sampling sequence from the first current waveform data as reference waveform data;

[0034] A second current waveform data acquisition module, used to acquire a preset number of cycles of second current waveform data from adjacent detection terminals on each side of the reference terminal based on the fault moment;

[0035] A target section extraction module, used to extract the target section with the highest waveform similarity to the reference waveform data from the second current waveform data of the adjacent detection terminal;

[0036] A reference waveform data extraction module, used to extract the time domain current sampling sequence of the target section as reference waveform data;

[0037] The fault location module is used to locate the fault according to the waveform similarity between the benchmark waveform data and each of the reference waveform data.

[0038] Optionally, the reference waveform data extraction module includes:

[0039] The reference waveform data extraction submodule is used to extract a time domain current sampling sequence from the first current waveform data as reference waveform data by using a preset number of sampling points.

[0040] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0041] The memory is used to store program code and transmit the program code to the processor;

[0042] The processor is used to execute the distributed fault location method for a closed-loop power distribution network as described in any one of the above items according to the instructions in the program code.

[0043] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program codes, and the program codes are used to execute the distributed fault location method for a closed-loop power distribution network as described in any one of the above items.

[0044] It can be seen from the above technical scheme that the present invention has the following advantages: the present invention also provides a distributed fault location method for a closed-loop distribution network, and specifically discloses: determining the detection terminal that first detects a current mutation as a reference terminal, and determining the moment when the current mutation is detected as the fault moment; obtaining the first current waveform data of the cycle at the fault moment from the reference terminal; extracting the time domain current sampling sequence from the first current waveform data as the reference waveform data; taking the fault moment as a reference, obtaining the second current waveform data of a preset number of cycles from the adjacent detection terminals on each side of the reference terminal; extracting the target section with the highest waveform similarity to the reference waveform data from the second current waveform data of each adjacent detection terminal; extracting the time domain current sampling sequence of the target section as the reference waveform data; and locating the fault according to the waveform similarity between the reference waveform data and each reference waveform data.

[0045] The present invention fully considers the influence of distributed power sources on the fault current of the distribution network by detecting current mutations in real time, acquiring and matching the reference waveform and reference waveform data, and performing similarity analysis, thereby being able to accurately identify fault signals and improve the sensitivity and reliability of fault location. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0047] Figure 1 This is a simplified diagram of a closed-loop distribution network with distributed generation;

[0048] Figure 2 It is a schematic diagram of the closed-loop operation distribution network when a fault occurs at F1;

[0049] Figure 3 A flowchart of the steps of a distributed fault location method for a closed-loop distribution network provided by an embodiment of the present invention;

[0050] Figure 4A flowchart of the steps of a distributed fault location method for a closed-loop distribution network provided by another embodiment of the present invention;

[0051] Figure 5 It is a flowchart of signal synchronization;

[0052] Figure 6 A structural block diagram of a distributed fault location device for a closed-loop distribution network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The embodiments of the present invention provide a method, device, equipment and medium for distributed fault location in a closed-loop distribution network, which are used to solve the technical problems of low accuracy and low sensitivity in fault location in a closed-loop distribution network.

[0054] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] See also Figure 3 , Figure 3 A flow chart of the steps of a distributed fault location method for a closed-loop distribution network provided by an embodiment of the present invention.

[0056] The present invention provides a method for locating a distributed fault in a closed-loop power distribution network, which may specifically include the following steps:

[0057] Step 301, determining the detection terminal that first detects the current mutation as the reference terminal, and determining the moment when the current mutation is detected as the fault moment;

[0058] In an embodiment of the present invention, the closed-loop distribution network is provided with a plurality of circuit breakers; each circuit breaker is provided with a detection terminal. When a fault occurs in the closed-loop distribution network, the detection terminal that first detects a sudden change in current can be determined as the reference terminal, and the moment when the sudden change in current is detected can be determined as the fault moment.

[0059] In the specific implementation, each detection terminal can perform current sampling at a frequency of 50Hz for current mutation detection. A sampling frequency of 50Hz means that the current can be measured 50 times per second. This high-frequency sampling ensures that the system can capture any sudden changes in the current in a timely manner, thereby realizing real-time monitoring of the current state. High-frequency sampling can provide more data points, making the characteristics of the current waveform clearer. This helps the system to more accurately analyze the changing trend of the current and reduce misjudgments or missed judgments due to insufficient data. In addition, in current mutation detection, fast response is crucial. The sampling frequency of 50Hz enables the system to detect current anomalies in a very short time, so as to quickly take necessary measures, such as cutting off the power supply, sounding an alarm, etc., to prevent the fault from expanding or causing more serious consequences.

[0060] Compared with the existing technology, this method uses the current mutation signal as the starting condition and combines the fault current waveform similarity analysis in the subsequent steps as the fault location criterion. Therefore, in actual operation, the fault current mutation value can be set to a lower threshold, which greatly improves the sensitivity and reliability of the traditional intelligent distributed method.

[0061] Step 302, obtaining first current waveform data of the cycle at the time of the fault from the reference terminal;

[0062] After the reference terminal is determined, the first current waveform data of the cycle at the time of the fault can be obtained from the reference terminal.

[0063] Step 303, extracting a time domain current sampling sequence from the first current waveform data as reference waveform data;

[0064] Time domain describes the relationship between mathematical functions or physical signals and time. For example, the time domain waveform of a signal can express how the signal changes over time.

[0065] In the embodiment of the present invention, a time-domain current sampling sequence may be extracted from the first current waveform data as the reference waveform data.

[0066] Step 304, taking the fault moment as a reference, respectively acquiring a preset number of cycles of second current waveform data from adjacent detection terminals on each side of the reference terminal;

[0067] After determining the fault moment, the second current waveform data of each preset number of cycles can be obtained from the adjacent detection terminals on each side of the reference terminal based on the fault moment. By collecting the second current data within a certain cycle range at the fault moment, it can be determined whether the adjacent detection terminals on each side of the reference terminal are affected by the fault traveling wave.

[0068] Step 305, extracting the target section having the highest waveform similarity to the reference waveform data from the second current waveform data of the adjacent detection terminal;

[0069] In a specific implementation, if a fault occurs between two detection terminals, the two detection terminals will have similar current waveforms. Therefore, the reference waveform can be compared with the second current waveform data of the adjacent detection terminal to find the section with the highest waveform similarity as the target section. Through the waveform similarity between the target section and the reference waveform, it can be determined whether the fault occurs between the two detection terminals.

[0070] Step 306, extracting the time domain current sampling sequence of the target section as reference waveform data;

[0071] Since the reference waveform data is a time-domain current sampling sequence, in order to compare the waveform similarity between the reference waveform data and the target section waveform data, the reference waveform data needs to be converted into a time-domain current sampling sequence.

[0072] Step 307 , performing fault location according to the waveform similarity between the benchmark waveform data and each reference waveform data.

[0073] According to the waveform similarity between the benchmark waveform data and the reference waveform data, the fault location can be narrowed down to between the two detection terminals, thereby achieving accurate positioning of the fault location.

[0074] The present invention fully considers the influence of distributed power sources on the fault current of the distribution network by detecting current mutations in real time, acquiring and matching the reference waveform and reference waveform data, and performing similarity analysis, thereby being able to accurately identify fault signals and improve the sensitivity and reliability of fault location.

[0075] See also Figure 4 , Figure 4 A flowchart of a method for locating a distributed fault in a closed-loop distribution network provided in another embodiment of the present invention may specifically include the following steps:

[0076] Step 401, determining the detection terminal that first detects the current mutation as the reference terminal, and determining the moment when the current mutation is detected as the fault moment;

[0077] Step 402, obtaining first current waveform data of the cycle at the time of the fault from the reference terminal;

[0078] Step 403, extracting a time domain current sampling sequence from the first current waveform data as reference waveform data by using a preset number of sampling points;

[0079] In a specific implementation, a time-domain current sampling sequence may be extracted from the first current waveform data according to a preset number of sampling points (eg, p sampling points) as the reference waveform data.

[0080] Step 404, determining whether the current flow directions of adjacent detection terminals on the same side of a single side of the reference terminal are the same;

[0081] Step 405, if they are the same, then starting from n cycles before the fault moment, respectively obtain the third current waveform data of m cycles of each adjacent detection terminal on the same side, where m>n;

[0082] Step 406, superimposing the third current waveform data on the same side to obtain second current waveform data;

[0083] In the embodiment of the present invention, when there are multiple adjacent terminals on one side of the reference terminal, due to the current shunting characteristics, the third current waveform data of the adjacent detection terminals on the same side where the current flows need to be superimposed to obtain complete second current waveform data.

[0084] For example, starting from n cycles before the fault moment, the third current waveform data of m cycles of adjacent detection terminals on the same side are respectively obtained and superimposed to obtain the second current waveform data, wherein m>n.

[0085] Preferably, 1≤n≤2; 3≤m<5. By adjusting the value of n, the sensitivity to the precursor of the current mutation can be adjusted. A smaller n value means that the system pays more attention to the data immediately before the mutation, while a larger n value provides a broader historical context, which helps to identify earlier change trends. Selecting an appropriate n value can reduce unnecessary data processing while maintaining calculation accuracy, thereby improving the overall efficiency of the system. The value range of m ensures that a sufficient number of continuous cycle data can be obtained for similarity analysis. More data points generally mean more accurate waveform feature extraction and higher fault identification accuracy. By including multiple continuous cycle data, the dynamic change characteristics of the current waveform can be more comprehensively captured, which is crucial for identifying different types of faults. In addition, different faults may cause waveform anomalies of different lengths. By adjusting the value of m within this range, the system can adapt to the needs of different fault scenarios and ensure that there is enough data for accurate analysis.

[0086] Step 407, extracting the target section with the highest waveform similarity to the reference waveform data from the second current waveform data of the adjacent detection terminal;

[0087] In practical applications, in order to ensure the accuracy of fault judgment, it is necessary to identify the current waveform emitted by the fault point at the time of the fault in the current waveform of the adjacent detection terminal, so that the fault judgment can be performed by analyzing the current waveforms generated at the same time collected by different detection terminals. Therefore, the reference waveform can be compared with the second current waveform data of the adjacent detection terminal to find the section with the highest waveform similarity as the target section. Through the waveform similarity between the target section and the reference waveform, it can be determined whether the fault occurs between the two detection terminals.

[0088] In one example, step 407 may include the following sub-steps:

[0089] S71, dividing the second current waveform data into a plurality of sections by using a sliding window with a preset number of sampling points and a preset number of sliding points;

[0090] S72, sequentially calculating the waveform similarity between the waveform data of each segment and the reference waveform data;

[0091] S73, determining the segment with the highest waveform similarity as the target segment.

[0092] In the specific implementation, a sliding window algorithm is used to find the segment with the highest similarity to the reference waveform data from the current waveform data of each adjacent terminal. In this way, similar waveform segments can be matched efficiently and accurately, the accuracy of fault location can be improved, the complexity of distributed power access can be adapted, and the robustness of the algorithm can be enhanced.

[0093] Among them, the window size of the sliding window algorithm is p sampling points, and q sampling points are slid each time. In this way, it is possible to achieve fine analysis of waveform data, efficient traversal speed, flexible similarity matching, the ability to adapt to different sampling frequencies and data lengths, and improve algorithm efficiency and accuracy. In specific implementation, 2<q<6; 25<p<50. In intelligent distributed fault processing, fault characteristics may be manifested in different forms of current waveforms, such as rapid mutations, periodic disturbances, etc. By setting the p value between 25 and 50 and the q value between 2 and 6, the sliding window algorithm can flexibly adapt to these different fault characteristics and accurately identify and locate faults. Reasonable p and q value settings help reduce misjudgments caused by factors such as data noise and sampling errors. The window size is large enough to smooth out some small fluctuations and noise; the sliding step size is moderate to avoid missing important information due to too large a step size or falling into a local optimum due to too small a step size.

[0094] The waveform similarity is calculated as follows:

[0095]

[0096] in, represents the waveform similarity between current sequence A and current sequence B, where current sequence A and current sequence B represent the reference waveform data and the waveform data in the segment intercepted by the sliding window respectively; Represents the Euclidean distance between two points; and are the i-th sampling current of current sequence A and current sequence B respectively; p represents the number of sampling points of current sequence A and current sequence B.

[0097] By calculating the Euclidean distance between two points and calculating the similarity between the entire current sequence based on these distances, this method can accurately reflect the similarity between two current waveforms. Euclidean distance is a common method for measuring the straight-line distance between points. In waveform analysis, it can effectively capture the differences and similarities of waveform characteristics. In addition, this method takes into account the current value of each sampling point in the current sequence, so it can fully capture the detailed characteristics of the waveform. Whether it is the overall trend of the waveform or the local changes, it can be accurately quantified and analyzed by this method. In addition, since this method is based on the sampling points of the current sequence for calculation, it is suitable for current waveform data with different sampling frequencies and resolutions. At the same time, by adjusting the number of sampling points p, the accuracy and calculation amount of the analysis can be further controlled.

[0098] Existing technologies often rely on high-speed communication media or physical peripherals such as GPS to ensure the timeliness of signal transmission, which makes the intelligent distributed method have great limitations and is also the main reason for the current small number of intelligent distributed applications. The embodiment of the present invention does not require high-precision timing in differential protection, and uses software algorithms to achieve data signal synchronization between terminals in intelligent distribution.

[0099] Step 408, extracting the time domain current sampling sequence of the target section as reference waveform data;

[0100] Step 409 : determining the section between the detection terminal and the reference terminal corresponding to the reference waveform data whose waveform similarity with the reference waveform data is less than a preset similarity threshold as a fault area.

[0101] If the fault occurs between two terminals, then the current waveform similarity of the two terminals at the moment of the fault is the lowest, because normally, if a fault occurs at a certain point, all the current on the line will flow here. At this time, the terminal current values ​​on both sides of the fault point will be completely different, and even the phase will be reversed. At this time, the calculated similarity is very low. Therefore, to determine whether the current of an adjacent terminal has a mutation characteristic, it can be determined whether the similarity between the reference waveform data of the adjacent terminal and the reference waveform data is less than a preset threshold. If so, it is determined that the current of the adjacent terminal has a mutation characteristic.

[0102] By comparing the similarity between the reference waveform data and the benchmark waveform data, the system can accurately identify whether the current has undergone a sudden change. This judgment method based on waveform similarity is more accurate than the traditional current threshold judgment and can capture more subtle current changes. The preset threshold is set as the basis for judgment, which can be adjusted according to the actual situation to meet the needs of different application scenarios. By optimizing the threshold setting, the system can improve the detection sensitivity of current mutations and ensure rapid response when a fault occurs. In addition, the judgment method based on waveform similarity can reduce false alarms and missed alarms caused by factors such as current fluctuations and noise interference. By comparing the similarity of the entire waveform, rather than just focusing on the change in current value, the system can more accurately determine whether the current has undergone a real sudden change. It can enhance the robustness of the system so that it can maintain stable performance in the face of complex and changeable power system environments. By accurately identifying current mutations, the system can more effectively respond to various fault conditions and ensure the safe and stable operation of the power system. In addition, in a distributed power system, each terminal may be distributed in different geographical locations. By comparing the waveform data of adjacent terminals, the system can achieve comprehensive monitoring and fault detection of the entire power system. This distributed fault handling method can improve the reliability and efficiency of the system and reduce the impact of faults on the power system.

[0103] Furthermore, in the embodiment of the present invention, after the fault area is determined, the following steps may be performed: disconnecting a circuit breaker corresponding to the fault area.

[0104] When the system detects a fault and locates it, it quickly disconnects the circuit breakers on both sides of the fault, which can immediately cut off the fault current and prevent the fault from further expanding or spreading to the entire system. This quick response is essential to protecting the safety of equipment and systems. Fault isolation can prevent the fault current from causing further damage to power equipment and systems. By disconnecting the power supply to the fault area, other normally operating equipment can be protected from the impact of the fault, and it also helps to reduce the system downtime caused by the fault. By quickly and accurately isolating the fault area, the system can resume normal operation as soon as possible, reducing the power outage time and impact on users caused by the fault.

[0105] Furthermore, in an embodiment of the present invention, a fault recovery strategy can also be formulated according to the network topology of the distribution network and the operating status before the fault, and the fault recovery strategy includes reconfiguring the network structure and restoring the power supply to the non-fault section. By formulating and executing the fault recovery strategy, the system can quickly reconfigure the network structure and restore the power supply to the non-fault section. This helps to reduce the power outage time, improve the user's power experience, and reduce the economic losses caused by power outages. The implementation of the fault recovery strategy can ensure that the system can resume normal operation as soon as possible when a fault occurs, thereby improving the power supply reliability of the entire distribution network. When formulating the fault recovery strategy, the system will consider the network topology of the distribution network and the operating status before the fault to select the optimal recovery plan. This helps to optimize the network structure and improve the operating efficiency and flexibility of the power grid.

[0106] The present invention fully considers the influence of distributed power sources on the fault current of the distribution network by detecting current mutations in real time, acquiring and matching the reference waveform and reference waveform data, and performing similarity analysis, thereby being able to accurately identify fault signals and improve the sensitivity and reliability of fault location.

[0107] For ease of understanding, the embodiments of the present invention are described below by using specific examples:

[0108] This example is Figure 2 A fault occurs at F in the figure. The detailed troubleshooting process is as follows:

[0109] When a fault occurs at F, the detection terminal where S1 is located first detects the current mutation, and the S1 detection terminal is used as the reference terminal;

[0110] Obtain current waveform data of three cycles starting from one cycle before the fault moment from the detection terminals of CB1 and S2 (i.e., two adjacent detection terminals of S1) respectively (at a power frequency of 50 Hz, the fault current data includes 32 sampling points in one cycle) as reference waveform data;

[0111] Using sliding window calculation, calculate the section with the highest similarity between the three-cycle (96 sampling points) current waveform provided by each adjacent detection terminal (CB1 and S2) and the current waveform of the fault terminal S1 as the reference waveform data;

[0112] For discrete sampling signals, assuming that the time domain current sampling sequences of S1 and S2 are A and B, they can be expressed as follows:

[0113] (1)

[0114] (2)

[0115] in, , They represent the actual sampling values ​​respectively, n is the number of sampling points for one cycle (32), and m is the number of sampling points for three cycles (96).

[0116] The 32 sampling points in the S1 current sequence A are used as the calculation window length. Four sampling points are selected each time the window is drawn. The correlation between each 32-window length sequence in the S2 current sequence B and the sequence A is calculated in turn. The segment in B that is most similar to the current sequence A is obtained by minimizing the correlation.

[0117] (3)

[0118] in, It represents one cycle data of the i-th window calculation of the current sequence B, which is expressed as follows:

[0119] (4)

[0120] The waveform similarity between current sequence A and current sequence B is expressed as follows:

[0121] (5)

[0122] in, Represents the Euclidean distance between two points.

[0123] Through equations (3), (4), and (5), we can obtain the segment with the highest correlation between the S2 current sequence B and the S1 current sequence A, which is denoted as (i.e. refer to waveform data).

[0124] Synchronous current waveform data A, , analyze the line operation status; realize the signal synchronization process such as Figure 5 shown.

[0125] By synchronizing the current waveform data in the above formula, the actual calculation time domain current sampling sequence A, , which can be expressed as follows:

[0126] (6)

[0127] (7)

[0128] Finally, we can analyze the time domain current sampling sequence A, Locate the fault section and isolate the fault.

[0129] It is worth noting that due to the bidirectional flow characteristics of the closed-loop distribution network system current, such as Figure 2When a fault occurs at point F and point F1, the fault current directions of the terminals where S2 and S3 are located are completely different. According to the content of patent invention S7, when a fault occurs at point F, the above calculations can be easily performed through the terminals where S1 and S2 are located to determine that the fault section is between S1 and S2. Multiple terminals within the range of the terminals where S2, S3, and S6 are located need to be jointly calculated, and the steps are as follows:

[0130] First, the S2 terminal recognizes that there are two adjacent terminal devices downstream through the topological relationship, and multi-terminal joint calculation is required for fault location;

[0131] In case of a fault at point F, the fault current at the S2 terminal is "outflowing" relative to the S2 terminal, and the fault current at the S3 and S6 terminals is "inflowing" relative to the S2 terminal;

[0132] To locate and determine the fault in this section, it is necessary to first superimpose the fault synchronous current data of the two terminals S3 and S6, both of which are in the "flowing" direction relative to each other, and then perform waveform similarity analysis with the terminal at S2 to determine whether the fault exists in this section.

[0133] See also Figure 6 , Figure 6 A structural block diagram of a distributed fault location device for a closed-loop distribution network provided by an embodiment of the present invention.

[0134] The embodiment of the present invention provides a distributed fault location device for a closed-loop distribution network. The closed-loop distribution network is provided with a plurality of circuit breakers; each circuit breaker is provided with a detection terminal; the device comprises:

[0135] A reference terminal and fault moment determination module 601 is used to determine the detection terminal that first detects the current mutation as the reference terminal, and determine the moment when the current mutation is detected as the fault moment;

[0136] A first current waveform data acquisition module 602, used to acquire first current waveform data of the cycle at the time of the fault from a reference terminal;

[0137] A reference waveform data extraction module 603 is used to extract a time domain current sampling sequence from the first current waveform data as reference waveform data;

[0138] The second current waveform data acquisition module 604 is used to acquire the second current waveform data of a preset number of cycles from the adjacent detection terminals on each side of the reference terminal based on the fault moment;

[0139] The target section extraction module 605 is used to extract the target section with the highest waveform similarity to the reference waveform data from the second current waveform data of the adjacent detection terminal;

[0140] A reference waveform data extraction module 606 is used to extract a time domain current sampling sequence of a target section as reference waveform data;

[0141] The fault location module 607 is used to locate the fault according to the waveform similarity between the benchmark waveform data and each reference waveform data.

[0142] In the embodiment of the present invention, the reference waveform data extraction module 603 includes:

[0143] The reference waveform data extraction submodule is used to extract a time domain current sampling sequence from the first current waveform data as the reference waveform data by using a preset number of sampling points.

[0144] In the embodiment of the present invention, the second current waveform data acquisition module 604 includes:

[0145] A current flow direction determination submodule is used to determine whether the current flow directions of adjacent detection terminals on the same side of a single side of the reference terminal are the same;

[0146] The third current waveform data acquisition submodule is used to respectively acquire the third current waveform data of m cycles of each adjacent detection terminal on the same side starting from n cycles before the fault moment if they are the same, where m>n;

[0147] The second current waveform data acquisition submodule is used to superimpose the third current waveform data on the same side to obtain the second current waveform data.

[0148] In the embodiment of the present invention, the target segment extraction module 605 includes:

[0149] A section division submodule, used to divide the second current waveform data into a plurality of sections through a sliding window with a preset number of sampling points and a preset number of sliding points;

[0150] A waveform similarity calculation submodule is used to sequentially calculate the waveform similarity between the waveform data of each section and the reference waveform data;

[0151] The target segment determination submodule is used to determine the segment with the highest waveform similarity as the target segment.

[0152] In this embodiment of the present invention, the fault location module 607 includes:

[0153] The fault location submodule is used to determine the section between the detection terminal and the reference terminal corresponding to the reference waveform data whose waveform similarity with the reference waveform data is less than a preset similarity threshold as the fault area.

[0154] In an embodiment of the present invention, it also includes:

[0155] The circuit breaker disconnecting module is used to disconnect the circuit breaker corresponding to the fault area.

[0156] An embodiment of the present invention further provides an electronic device, the device comprising a processor and a memory:

[0157] The memory is used to store the program code and transmit the program code to the processor;

[0158] The processor is used to execute the distributed fault location method for the closed-loop power distribution network according to the instructions in the program code.

[0159] An embodiment of the present invention further provides a computer-readable storage medium, which is used to store program codes, and the program codes are used to execute the distributed fault location method for a closed-loop power distribution network according to an embodiment of the present invention.

[0160] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0161] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0162] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0163] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0164] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0166] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0167] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0168] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0169] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distributed fault location method for a closed-loop distribution network, characterized in that: The closed-loop power distribution network is provided with a plurality of circuit breakers; each of the circuit breakers is provided with a detection terminal; the method comprises: The detection terminal that first detects the current mutation is determined as the reference terminal, and the moment when the current mutation is detected is determined as the fault moment; Acquire the first current waveform data of the cycle at the time of the fault from the reference terminal; Extracting a time-domain current sampling sequence from the first current waveform data as reference waveform data; Taking the fault moment as a reference, obtaining a preset number of cycles of second current waveform data from adjacent detection terminals on each side of the reference terminal; Extracting a target section having the highest waveform similarity to the reference waveform data from the second current waveform data of the adjacent detection terminal; Extracting a time-domain current sampling sequence of the target section as reference waveform data; Fault location is performed based on the waveform similarity between the benchmark waveform data and each of the reference waveform data.

2. The method according to claim 1, characterized in that The step of extracting a time domain current sampling sequence from the first current waveform data as reference waveform data comprises: A time domain current sampling sequence is extracted from the first current waveform data as reference waveform data by presetting the number of sampling points.

3. The method according to claim 1, characterized in that The step of obtaining a preset number of cycles of second current waveform data from adjacent detection terminals on each side of the reference terminal based on the fault moment comprises: Determining whether current flow directions of adjacent detection terminals on the same side of a single side of the reference terminal are the same; If they are the same, starting from n cycles before the fault moment, the third current waveform data of m cycles of each adjacent detection terminal on the same side are respectively obtained, wherein m>n; The third current waveform data on the same side is superimposed to obtain the second current waveform data.

4. The method according to claim 1, characterized in that: The step of extracting the target section having the highest waveform similarity to the reference waveform data from the second current waveform data of the adjacent detection terminal comprises: Dividing the second current waveform data into a plurality of sections through a sliding window with a preset number of sampling points and a preset number of sliding points; sequentially calculating the waveform similarity between the waveform data of each segment and the reference waveform data; The segment with the highest waveform similarity is determined as the target segment.

5. The method according to claim 1, characterized in that The step of locating the fault according to the waveform similarity between the benchmark waveform data and each of the reference waveform data comprises: A section between the detection terminal and the reference terminal corresponding to the reference waveform data whose waveform similarity with the reference waveform data is less than a preset similarity threshold is determined as a fault area.

6. The method according to claim 5, characterized in that After the step of determining as a fault area the section between the detection terminal and the reference terminal corresponding to the reference waveform data whose waveform similarity with the reference waveform data is less than a preset similarity threshold, the method further includes: Disconnect the circuit breaker corresponding to the fault area.

7. A distributed fault location device for a closed-loop distribution network, characterized in that: The closed-loop power distribution network is provided with a plurality of circuit breakers; each circuit breaker is provided with a detection terminal; the device comprises: A reference terminal and fault moment determination module, used to determine the detection terminal that first detects the current mutation as the reference terminal, and determine the moment when the current mutation is detected as the fault moment; A first current waveform data acquisition module, used to acquire first current waveform data of the cycle at the time of the fault from the reference terminal; A reference waveform data extraction module, used to extract a time domain current sampling sequence from the first current waveform data as reference waveform data; A second current waveform data acquisition module, used to acquire a preset number of cycles of second current waveform data from adjacent detection terminals on each side of the reference terminal based on the fault moment; A target section extraction module, used to extract the target section with the highest waveform similarity to the reference waveform data from the second current waveform data of the adjacent detection terminal; A reference waveform data extraction module, used to extract the time domain current sampling sequence of the target section as reference waveform data; The fault location module is used to locate the fault according to the waveform similarity between the benchmark waveform data and each of the reference waveform data.

8. The device according to claim 7, characterized in that The reference waveform data extraction module comprises: The reference waveform data extraction submodule is used to extract a time domain current sampling sequence from the first current waveform data as reference waveform data by using a preset number of sampling points.

9. An electronic device, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the distributed fault location method for a closed-loop distribution network according to any one of claims 1 to 6 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program codes, and the program codes are used to execute the distributed fault location method for a closed-loop distribution network according to any one of claims 1 to 6.

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