Intelligent distributed power distribution network fault positioning method based on Internet of Things

Abstract

The invention discloses an intelligent distributed power distribution network fault positioning method based on the Internet of Things, relates to the technical field of power grid fault detection, and solves the technical problem of low fault positioning efficiency caused by the fact that fault positioning needs to be carried out through a master station when detection is carried out based on traveling wave signals in the prior art. The method comprises the following steps: detecting a traveling wave signal through a traveling wave detection device deployed in a power distribution network, and extracting the arrival time of the traveling wave signal; and the traveling wave detection device realizes fault positioning according to the extracted arrival time and the arrival time extracted by the adjacent traveling wave detection device. Compared with the prior art that the traveling wave detection devices send the arrival time of the traveling wave signals to the master station, and the master station carries out fault positioning according to the arrival time, data processing is distributed in the traveling wave detection devices, the obtaining time of at least one arrival time can be saved, and the fault positioning efficiency can be improved; moreover, the traveling wave detection device responsible for fault positioning can be determined according to the extraction sequence of the arrival time, and the fault positioning efficiency is further improved.

Description

Technical Field

[0001] This invention belongs to the field of power grid fault detection, specifically a method for fault location in intelligent distributed distribution networks based on the Internet of Things. Background Technology

[0002] As the power grid continues to expand and become more complex, various types of faults are prone to occur in the distribution network. These faults are usually caused by aging power grid lines, external environmental influences, or equipment damage. When a fault occurs, it can lead to current imbalance, line short circuits, or even large-scale power outages. Therefore, how to quickly and accurately detect and locate single-phase grounding faults is a key link in ensuring power grid safety and reducing power outage time.

[0003] In existing technologies for fault location in distribution networks based on traveling wave signal detection, the master station receives the arrival time of the traveling wave signal sent by the traveling wave detection device and uses single-end ranging or multi-end ranging methods to locate the fault location in the transmission line. However, in existing technologies, after detecting the arrival time of the traveling wave signal, the traveling wave detection device still needs to send the arrival time to the master station for analysis to achieve fault location; if multi-end ranging is used, it is necessary to wait for all arrival times of the traveling wave signal to be received before fault location can be performed, which wastes time and affects the fault location efficiency of the distribution network.

[0004] This invention provides a method for fault location in an intelligent distributed power distribution network based on the Internet of Things, which is used to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a smart distributed distribution network fault location method based on the Internet of Things (IoT). This method uses traveling wave detection devices deployed in the distribution network to detect traveling wave signals and extract their arrival times. The traveling wave detection devices locate faults based on the extracted arrival times and those extracted by adjacent traveling wave detection devices, thus improving fault location efficiency. Furthermore, the method can determine the traveling wave detection device responsible for fault location based on the order of arrival time extraction, further improving fault location efficiency. This solves the technical problem of low fault location efficiency in existing technologies that rely on the master station receiving the arrival times extracted by the traveling wave detection devices before fault location.

[0006] To achieve the above objectives, a first aspect of the present invention provides a method for fault location in an intelligent distributed distribution network based on the Internet of Things, comprising: The traveling wave detection device identifies the arrival time of the traveling wave signal. The traveling wave detection device receives the arrival time two of the traveling wave signal; wherein, the arrival time two is identified by a traveling wave detection device adjacent to the traveling wave detection device in the distribution network; The traveling wave detection device locates faults based on arrival time one and arrival time two.

[0007] Preferably, the traveling wave detection devices are clock synchronized, and the clock error after synchronization should be less than 1. .

[0008] Preferably, when the propagation speed of the traveling wave signal in the transmission line is fixed, the traveling wave detection device performs fault location based on arrival time one and arrival time two, including: Obtain the propagation speed of traveling wave signals in transmission lines Arrival time one and arrival time two are marked as follows: and ; Through formula Calculate the distance between the fault point and the arrival time corresponding to the traveling wave detection device. ;in, This refers to the length of the power transmission line.

[0009] Preferably, when the propagation speed of the traveling wave signal changes in the transmission line, the traveling wave detection device performs fault location based on arrival time one and arrival time two, including: The propagation speed and length of the traveling wave signal in each segment of the transmission line are extracted and labeled as follows: and ;in, , Mark the line segment; Based on arrival time 1 and arrival time 2, as well as the propagation speed of each line segment, the line segment where the fault point is located is determined and marked as follows: ; pass Calculated The distance between the fault point and the arrival time corresponds to the distance of the traveling wave detection device. ;in, and These are arrival time one and arrival time two, respectively.

[0010] Preferably, calculating the propagation speed of the traveling wave signal in each segment of the transmission line includes: Retrieve the velocity estimation model; the velocity estimation model is constructed based on an artificial intelligence model. The transmission line between adjacent traveling wave detection devices is divided into several line segments, and the line parameter characteristics and environmental parameter characteristics of several line segments are extracted. The line parameter features and environmental parameter features of the line segment are integrated into a vector, which is then input into an artificial intelligence model to obtain the speed correction coefficient of the corresponding line segment; the propagation speed of each line segment is obtained based on the speed correction coefficient and the speed of light.

[0011] Preferably, the transmission lines between adjacent traveling wave detection devices are divided into several line segments according to a set distance or line type.

[0012] Preferably, after obtaining the propagation speed of each line segment based on the speed correction coefficient and the speed of light, it is determined whether the difference in propagation speed between adjacent line segments is less than a set threshold; if so, the adjacent line segments are merged into one line segment, and the propagation speeds of the adjacent line segments are weighted and summed as the propagation speed of the merged line segment.

[0013] Preferably, the propagation speeds of adjacent line segments are weighted and summed, including: Extract the line lengths of adjacent line segments, normalize the two line lengths, and use them as weight coefficients, labeled as follows: and The propagation speeds of the two line segments are respectively marked as... and ; Through formula The propagation speed of the merged line segment was calculated. ;in, .

[0014] Preferably, the velocity estimation model is built based on an artificial intelligence model, including: Collect test data including line parameter data, environmental parameter data, and speed correction coefficient of the actual measured traveling wave signal; among which, line parameter data includes capacitance and inductance per unit length, and environmental parameter data includes temperature, humidity, and air pressure; Through data cleaning and feature extraction, line parameter features and environmental parameter features are extracted from line parameter data and environmental parameter data, respectively. The characteristics of line parameters and environmental parameters are integrated into a vector and used as the input of the artificial intelligence model; the speed correction coefficients are integrated into a vector and used as the output of the artificial intelligence model; the artificial intelligence model is trained to obtain the speed estimation model; the artificial intelligence model includes a random forest model or a deep convolutional neural network model.

[0015] Preferably, the fault location is determined based on arrival time one, arrival time two, and the propagation speed of each line segment, including: Calculate each line segment The longest time it takes for the traveling wave signal at the fault point to propagate to traveling wave detection device 1 and traveling wave detection device 2 are respectively: , ; based on , Determine the line segment The fault point is located on the line section. middle.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention detects traveling wave signals using traveling wave detection devices deployed in the power distribution network and extracts the arrival times of the traveling wave signals. The traveling wave detection devices then locate faults based on the extracted arrival times and those extracted by adjacent traveling wave detection devices. Compared to existing technologies where the traveling wave detection devices send the arrival times of the traveling wave signals to the master station, and the master station locates faults based on the arrival times, this invention distributes data processing across each traveling wave detection device, saving at least one time spent acquiring arrival times and improving fault location efficiency. Furthermore, the order in which the traveling wave detection devices are extracted can be used to determine the responsible fault location device, further improving fault location efficiency.

[0017] 2. In fault location, this invention comprehensively considers the line characteristics and environmental characteristics of the transmission line between adjacent traveling wave detection devices, and combines an artificial intelligence model to estimate the influence of line characteristics and environmental characteristics on the transmission of traveling wave signals, thereby estimating the propagation speed of the traveling wave signals. Compared with existing technologies that rely on a fixed propagation speed for fault location, this invention improves the accuracy of fault location. Moreover, for complex transmission lines, this invention estimates the propagation speed in segments, further improving the reliability of the propagation speed and thus further enhancing the accuracy of fault location. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the method steps of the Internet of Things-based intelligent distributed power distribution network fault location method in this invention; Figure 2 This is a schematic diagram illustrating the interaction between adjacent traveling wave detection devices in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the process of obtaining the propagation speed of each line segment in an embodiment of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1: Please see Figure 1 The first aspect of this invention provides a method for fault location in an intelligent distributed distribution network based on the Internet of Things, comprising: S100: The traveling wave detection device identifies the arrival time of the traveling wave signal; S200: The traveling wave detection device receives the arrival time two of the traveling wave signal; wherein, the arrival time two is identified by a traveling wave detection device adjacent to the traveling wave detection device in the distribution network; S300: The traveling wave detection device locates faults based on arrival time one and arrival time two.

[0022] Before the traveling wave detection device in S100 can detect traveling wave signals, it is necessary to deploy traveling wave detection devices at key nodes in the distribution network. Key nodes include line segmentation points, branch nodes, tie switches, and cable-overhead line connections.

[0023] The deployment location of traveling wave detection devices directly affects the fault location accuracy of the distribution network, and requires comprehensive deployment based on the distribution network's line structure, frequent fault locations, and other factors.

[0024] Generally, critical nodes are identified based on the distribution network topology, and traveling wave detection devices should be deployed at these critical nodes. Critical nodes mainly include line segmentation points, branch points, tie switches, and cable-overhead line connections.

[0025] In special circumstances, to ensure accurate fault location, traveling wave detection devices need to be installed at predetermined intervals along long lines. Additionally, traveling wave detection devices also need to be deployed at frequently faulty locations, such as older line sections and areas prone to lightning strikes.

[0026] For example, traveling wave detection devices are deployed in at least the following typical locations.

[0027] 1) The outgoing switch of the substation is used as a reference point for traveling wave ranging; 2) At the section switch or tie switch, the power distribution line is divided into multiple sections to narrow down the fault location range; 3) The branch line entrance (branch point) is used to identify branch line faults and avoid the impact of misoperation on the main line; 4) In old line sections and areas prone to lightning strikes, it is used for real-time monitoring of partial discharge or traveling wave signals caused by lightning strikes.

[0028] Traveling wave detection devices are key equipment in power systems used for monitoring and analyzing fault traveling wave signals, mainly for the acquisition, processing, and location of fault traveling wave signals. The traveling wave detection device of this invention can be an upgrade of existing distribution network equipment or a stand-alone traveling wave detection device. The choice of a traveling wave detection device is based on a combination of cost and fault location accuracy.

[0029] For example, adding a transient traveling wave acquisition unit to a traditional FTU (feeder terminal unit) can also achieve the detection of traveling wave signals, and the modification cost is low; a transient traveling wave detection unit can also be added to a TTU (transformer terminal unit) to collect transient voltage traveling waves on the low-voltage side, which is suitable for fault early warning at the substation level in low-voltage distribution networks.

[0030] Of course, stand-alone traveling wave detection units (DTUs) can also be deployed at critical nodes, while cable-specific traveling wave detection units can be deployed along cable lines. The choice of traveling wave detection unit is not limited to a specific type or model; any device capable of performing traveling wave detection at the corresponding location and cooperating with other traveling wave detection units to complete fault location is acceptable.

[0031] It is worth noting that, in order to improve the accuracy of fault location in the distribution network, this invention employs multi-terminal traveling wave detection to achieve precise location. Therefore, the traveling wave detection devices can exchange data in real time through Internet of Things (IoT) technologies (4G / 5G, WIFI, LoRa, etc.). Furthermore, the traveling wave detection devices need to be clock-synchronized, such as through GPS or BeiDou. The clock error after synchronization should be less than 1. .

[0032] For example, according to the formula In overhead lines ( ) in 1 The positioning error is approximately 150m.

[0033] S100: The traveling wave detection device identifies the arrival time of the traveling wave signal.

[0034] When a fault occurs in a transmission line in a power distribution network, a high-frequency transient traveling wave signal is generated. These traveling waves propagate to both ends of the transmission line at a certain speed, so traveling wave detection devices can detect the traveling wave signal.

[0035] In complex power distribution networks, multiple terminals need to measure distances simultaneously to locate faults. Therefore, after a traveling wave detection device detects a traveling wave signal, it is also necessary for its adjacent traveling wave detection devices to detect the same traveling wave signal. The location of the fault point is calculated based on the arrival time of the traveling wave signals detected by the two traveling wave detection devices.

[0036] Adjacent traveling wave detection devices are defined in relation to transmission lines. That is, in any functional transmission line, if current flows through one traveling wave detection device, it may also flow through another traveling wave detection device; thus, the two can be considered adjacent traveling wave detection devices. Therefore, adjacent traveling wave detection devices are not simply defined by spatial proximity.

[0037] There may be multiple adjacent traveling wave detection devices corresponding to a traveling wave detection device, indicating that several transmission lines are connected at that traveling wave detection device. The traveling wave detection device locates faults based on arrival time one and arrival time two, therefore the traveling wave detection device has data processing capabilities.

[0038] It is worth noting that if a traveling wave detection device detects a traveling wave signal, and its adjacent traveling wave detection device does not detect a corresponding traveling wave signal within a set time, it can be determined that there is no fault in the transmission line between the traveling wave detection devices. In this case, it is necessary to determine whether the single-ended power supply line corresponding to the traveling wave detection device is faulty; the single-ended traveling wave method can be used for fault location.

[0039] S200: The traveling wave detection device receives the arrival time two of the traveling wave signal; wherein, the arrival time two is obtained by the traveling wave detection device adjacent to the traveling wave detection device in the distribution network.

[0040] Typical traveling wave detection devices are only responsible for detecting traveling wave signals. After detection, the identification results of the traveling wave signal, especially the arrival time of the traveling wave signal at the traveling wave detection device, need to be sent to the master station, which will then locate the fault based on the arrival time.

[0041] Taking adjacent traveling wave detection devices 1 and 2 as an example, if traveling wave detection device 1 detects a traveling wave signal, it sends the arrival time 1 of the traveling wave signal to the master station. After receiving arrival time 1, the master station needs to wait for the traveling wave signal to be detected by traveling wave detection device 2, and for traveling wave detection device 2 to send arrival time 2 to the master station. Only then can the master station perform fault location based on arrival time 1 and arrival time 2. In this example, it is clear that the master station needs to wait to receive two arrival times before it can perform fault location, which will cause a certain amount of time waste.

[0042] It is worth noting that when arrival time one is later than arrival time two, it means that before the traveling wave detection device identifies arrival time one, its adjacent traveling wave detection device has already (or simultaneously) identified arrival time two and sent arrival time two to the traveling wave detection device that identified arrival time one.

[0043] Please see Figure 2If any one of the adjacent traveling wave detection devices detects a traveling wave signal, it marks the arrival time of that signal as arrival time two and sends the identified arrival time two to the adjacent traveling wave detection device. Once the adjacent traveling wave detection device identifies the arrival time of the same traveling wave signal, it marks it as arrival time one. The identified arrival time one and the received arrival time two are then combined for fault location.

[0044] When arrival time one is later than arrival time two, the traveling wave detection device receives arrival time two from the adjacent traveling wave detection device while simultaneously identifying arrival time one. The extraction of arrival time one and the extraction and transmission of arrival time two overlap in time. Since both arrival time one and arrival time two are present simultaneously in the traveling wave detection device, fault location can be performed immediately, saving time and improving efficiency.

[0045] In some other preferred embodiments, arrival time one may be earlier than arrival time two, indicating that after the traveling wave detection device identifies the arrival time one of the traveling wave signal, it waits to receive arrival time two determined by the adjacent traveling wave detection device. Fault location is then performed after the traveling wave detection device receives arrival time two.

[0046] Obviously, in this embodiment, it is not necessary to send arrival time one and arrival time two to the main station for fault location. Fault location can be achieved in any of the adjacent traveling wave detection devices. Moreover, it is not necessary to wait for the extraction and transmission of two arrival times, but only one arrival time. Therefore, compared with the prior art, it can save time and improve the efficiency of fault location.

[0047] It should be noted that when the traveling wave detection device locates the fault based on arrival time one and arrival time two, the traveling wave signals corresponding to the two arrival times (both are initial traveling waves) should be consistent. Therefore, the waveform parameters of the traveling wave signal, such as one or more of the frequency and amplitude, can be sent together when sending the arrival time.

[0048] S300: The traveling wave detection device locates faults based on arrival time one and arrival time two.

[0049] When a fault occurs at a certain location in a transmission line, a high-frequency transient traveling wave signal is generated. This traveling wave signal will propagate bidirectionally along the transmission line. Two traveling wave detection devices in the line section containing the transmission line can detect the traveling wave signal, and thus the arrival time one and arrival time two can be extracted.

[0050] There are two scenarios when locating faults: if the characteristic parameters of the transmission lines between adjacent traveling wave detection devices do not change and the distance is short enough that their environmental conditions are not significantly different, then the propagation speed of the traveling wave signal in this transmission line can be considered fixed; otherwise, the propagation speed is considered to be variable.

[0051] When the propagation speed of a traveling wave signal in a transmission line is constant, the principle of fault location based on arrival time one and arrival time two is as follows: ;in, The distance from the fault point to traveling wave detection device A (traveling wave detection device 1) is... and These represent the arrival times of the traveling wave signal (initial traveling wave, not reflected or refracted traveling wave) at traveling wave detection device 1 and traveling wave detection device 2, respectively, corresponding to the aforementioned arrival time 1 and arrival time 2. L is the length of the transmission line between traveling wave detection device 1 and traveling wave detection device 2. To determine the propagation speed of a traveling wave signal, the line parameters and environmental parameters of the transmission line can be input into the speed estimation model to obtain the speed correction coefficient. This coefficient can then be multiplied by the speed of light to obtain the propagation speed. Alternatively, it can be determined through testing or based on experience.

[0052] It is worth noting that when a traveling wave detection device detects a traveling wave signal and sends the identification result (including the arrival time of the extracted traveling wave signal) to an adjacent traveling wave detection device, if the adjacent traveling wave detection device has not yet received the identification result but has already detected the traveling wave signal, and also sends the extracted identification result (including the arrival time of the extracted traveling wave signal) to its adjacent traveling wave detection device, it is necessary to clarify which traveling wave detection device should perform fault location.

[0053] In a preferred embodiment, after the traveling wave detection device sends the identified arrival time to the adjacent traveling wave detection device, when it receives the arrival time of the traveling wave signal sent by the adjacent traveling wave detection device, the adjacent traveling wave detection device performs fault location based on arrival time one and arrival time two.

[0054] For example, taking adjacent traveling wave detection devices 1 and 2 as an example, if traveling wave detection device 1 detects a traveling wave signal, it sends the identification result of the traveling wave signal (including arrival time 1) to traveling wave detection device 2; before traveling wave detection device 2 receives arrival time 1, it has already extracted arrival time 2 of the traveling wave signal and sent it to traveling wave detection device 1. If arrival time 1 and arrival time 2 belong to the same traveling wave signal, then traveling wave detection device 2 performs fault location.

[0055] If the traveling wave detection device 1 does not receive a successful positioning signal within the set time, it will perform fault location based on arrival time 1 and arrival time 2. This set time can be determined empirically, such as by statistically analyzing the historical data transmission records of traveling wave detection devices 1 and 2.

[0056] Traveling wave detection device one and traveling wave detection device two communicate with each other. When traveling wave detection device two completes fault location, it sends a location success signal to traveling wave detection device one. If traveling wave detection device one does not receive a location success signal within a set time range, it may be unable to complete fault location due to slow processing efficiency or a malfunction. To improve the efficiency of fault location, traveling wave detection device one performs fault location and also sends a location success signal to traveling wave detection device two after successful location. At this time, traveling wave detection device two can terminate its fault location process.

[0057] Example 2: Compared with Example 1, this example calibrates the propagation speed of the traveling wave signal between traveling wave detection device 1 and traveling wave detection device 2 to improve fault location accuracy.

[0058] When locating a fault based on arrival time one and arrival time two, the propagation speed of the traveling wave signal in the transmission line is crucial. As shown in the above calculation formula, the propagation speed is critical to the accuracy of fault location. The propagation speed of a traveling wave signal can ideally approximate the speed of light, but in reality, it is constrained by various factors. In practice, the propagation speed of a traveling wave signal is mainly determined by the following factors: 1) The electrical parameters of the transmission line are the decisive factors affecting the propagation speed of traveling wave signals. The smaller the inductance or capacitance of the transmission line, the faster the propagation speed.

[0059] 2) The physical structure of transmission lines also affects the propagation speed. For example, the conductors of overhead transmission lines are erected on poles and towers and are air-insulated; the conductors of high-voltage cable lines are wrapped in insulation layers and metal sheaths and buried underground; the propagation speed of traveling wave signals in overhead transmission lines is 80%-90% of the speed of light, while the propagation speed of traveling wave signals in high-voltage cable lines is 30%-50% of the speed of light.

[0060] 3) Environmental and operating conditions also affect propagation speed. Temperature changes affect the geometry of transmission line conductors and the properties of the insulation medium. For example, when the conductor temperature rises, the spacing may increase slightly (increasing inductance), and the dielectric constant of the insulation medium may change (changing capacitance).

[0061] In a preferred embodiment, the propagation speed of the traveling wave signal is estimated based on an artificial intelligence model. The training process of the artificial intelligence model is as follows: S211: Collect line parameter data, environmental parameter data, and speed correction coefficient of the actual measured traveling wave signal from fault or test data (the speed correction coefficient is multiplied by the speed of light to obtain the actual propagation speed relative to the speed of light). S212: Extract line parameter features and environmental parameter features from line parameter data and environmental parameter data respectively through data cleaning and feature extraction; S213: Integrate the line parameter features and environmental parameter features into a vector, which serves as the input to the artificial intelligence model; integrate the velocity correction coefficients of the actually measured traveling wave signal propagation speed into a vector, which serves as the output of the artificial intelligence model. The artificial intelligence model includes a random forest model or a deep convolutional neural network model.

[0062] Line parameters include estimated values ​​of inductance and capacitance per unit length, which can be obtained through theoretical calculations based on the line structure or through measured methods based on frequency domain response. Environmental parameters include temperature, humidity, and air pressure, which can be obtained from weather stations or through smart sensors.

[0063] Data cleaning and feature extraction methods can refer to existing technologies, and will not be elaborated here.

[0064] In some other preferred embodiments, traveling wave signal data can also be extracted from fault or test data, and the traveling wave signal can be determined as either a voltage traveling wave or a current traveling wave based on the traveling wave signal data. The type of the traveling wave signal is encoded (e.g., voltage traveling wave is encoded as 0, current traveling wave is encoded as 1), and integrated with line parameter features and environmental parameter features into a vector, which is used as input to the artificial intelligence model.

[0065] Traveling wave signal data includes arrival time of the first wave front, wave front rise rate, and spectral distribution. The waveform signal characteristics can lead to dispersion / nonlinear effects, which can also affect the propagation speed of the traveling wave signal to some extent.

[0066] In a preferred embodiment, when the propagation speed of the traveling wave signal in the transmission line is fixed, an artificial intelligence model is retrieved; the line parameter characteristics, environmental parameter characteristics, and traveling wave signal characteristics of the transmission line between traveling wave detection device one and traveling wave detection device two are integrated and input into the artificial intelligence model to obtain the speed correction coefficient of the traveling wave signal in the transmission line; the propagation speed of the traveling wave signal is obtained by multiplying the obtained speed correction coefficient by the speed of light, and then substituted into the... Calculated in , The distance from the fault point to traveling wave detection device 1 (traveling wave detection device 1) is the distance.

[0067] The parameters of a transmission line are not necessarily uniformly distributed. For example, an actual transmission line may consist of conductors of different types (e.g., different cross-sectional areas, number of splits), tower structures (e.g., different spans, heights), or laying methods (e.g., a mixed overhead and cable line), resulting in variations in inductance and capacitance in different sections. Moreover, the same line may traverse different terrains (e.g., plains, mountains) or climate zones (e.g., humid areas, icing areas), and environmental factors (e.g., humidity, temperature, icing) can alter local inductance and capacitance, and also affect propagation speed.

[0068] In a preferred embodiment, please refer to Figure 3 When the propagation speed of a traveling wave signal changes in a transmission line, the propagation speed can be estimated in segments using an artificial intelligence model. This specifically includes the following steps: S221: Divide the transmission line between adjacent traveling wave detection devices into several line segments at equal intervals, and extract the line parameter features and environmental parameter features of several line segments; S221: Integrate the traveling wave signal characteristics with the line parameter characteristics and environmental parameter characteristics of each line segment into a vector, input it into the artificial intelligence model to obtain the speed correction coefficient of the corresponding line segment; thereby estimating the propagation speed of each line segment.

[0069] In some other preferred embodiments, the transmission line between adjacent traveling wave detection devices can be divided into several line segments according to the line type, such as overhead lines, river-crossing lines, etc.

[0070] After obtaining the propagation speed of each line segment based on the speed correction coefficient and the speed of light, it is determined whether the difference in propagation speed between adjacent line segments is less than a set threshold; if so, the adjacent line segments are merged into one line segment, and the propagation speeds of the adjacent line segments are weighted and summed as the propagation speed of the merged line segment.

[0071] If the propagation speeds of adjacent line segments differ only slightly, they can be merged to reduce the data processing load during fault location. The merged propagation speeds are obtained through weighted summation.

[0072] In a preferred embodiment, the propagation speeds of adjacent line segments are weighted and summed, including: S231: Extract the line lengths of adjacent line segments, normalize the two line lengths, and use them as weight coefficients, labeled as follows. and The propagation speeds of the two line segments are respectively marked as... and ;like , ; S232: Through formula The propagation speed of the merged line segment was calculated. ;in, .

[0073] It should be noted that when adjacent line segments have the same length, then... .

[0074] After estimating the propagation speed of the traveling wave signal for each segment of the transmission line, the steps for fault location are as follows: S241: Determine the line segment where the fault point is located based on arrival time one, arrival time two, and the propagation speed of each line segment; S242: Determine the location of the fault point in the corresponding line segment based on arrival time one, arrival time two, and the propagation speed of each line segment.

[0075] For example, suppose the total length of the transmission line between traveling wave detection device one and traveling wave detection device two is... The total length of each line segment is The propagation speed of each line segment is , This refers to the number of line segments. If the fault point is related to a line segment... The distance from the fault point to the traveling wave detection device is... satisfy: .

[0076] The fault location can be determined based on a time window segmented by speed, including: Calculate each line segment The longest time it takes for the traveling wave signal at the fault point to propagate to traveling wave detection device 1 and traveling wave detection device 2 are respectively: , ; If the fault is in the line section Inside, then , You can also set constraints. Based on these constraints, it is possible to determine which segment of the transmission line the fault point is located on.

[0077] When the fault point on the line segment is determined When inside, then , The result can be calculated using these two formulas. , The distance between the fault point and the traveling wave detection device is... The distance to the traveling wave detection device two is .

[0078] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for fault location of smart distributed power distribution network based on Internet of Things, characterized in that, The application relates to a fault locating method and device for a power transmission line. The traveling wave detection device identifies the arrival time of a traveling wave signal; The traveling wave detection device receives the arrival time of the traveling wave signal; wherein the arrival time is identified by a traveling wave detection device adjacent to the traveling wave detection device in the power transmission line; The traveling wave detection device locates the fault point based on the arrival time and the arrival time.

2. The IoT based smart distributed power distribution network fault location method as claimed in claim 1 wherein, Clocks of the traveling wave detection devices are synchronized, and a clock error after synchronization should be less than 1 .

3. The IoT based smart distributed power distribution network fault location method as claimed in claim 1 wherein, When the traveling wave signal propagates at a fixed speed in the power transmission line, the traveling wave detection device locates the fault point based on the arrival time and the arrival time, including: acquiring a propagation speed of the traveling wave signal in the power transmission line , the arrival time one and the arrival time two are respectively marked as and ; The distance between the fault point and the corresponding traveling wave detection device is calculated by the formula ; wherein, ; wherein, is the line length of the power transmission line.

4. The IoT based smart distributed power distribution network fault location method as claimed in claim 1 wherein, When the traveling wave signal propagates at a variable speed in the power transmission line, the traveling wave detection device locates the fault point based on the arrival time and the arrival time, including: Extract the propagation speed of the traveling wave signal in each line section of the power transmission line and the length of the line section, respectively, and mark them as and ; wherein , is the line section mark; determining the line section where the fault point is located based on the first arrival time and the second arrival time and the propagation speed of each line section, and marking as ; By The calculation obtains The fault point corresponds to the distance of the traveling wave detection device to the arrival time ; wherein, And Arrival time one and arrival time two respectively.

5. The IoT-based smart distributed power distribution network fault location method according to claim 4, wherein, The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line.

6. The IoT-based smart distributed power distribution network fault location method according to claim 5, wherein, The application relates to a fault locating method and device for a power transmission line.

7. The IoT-based smart distributed power distribution network fault location method according to claim 5, wherein, The application relates to a fault locating method and device for a power transmission line.

8. The IoT-based smart distributed power distribution network fault location method according to claim 7, wherein, The application relates to a fault locating method and device for a power transmission line. Extract the line length of the adjacent line segment, and the two line lengths are normalized as weight coefficients, respectively marked as and ; the propagation speeds of the two line segments are respectively marked as and ; The propagation speed of the merged line segment is calculated by the formula ; wherein, ; wherein, .

9. The IoT-based smart distributed power distribution network fault location method according to claim 1, wherein, The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line.

10. The IoT-based smart distributed power distribution network fault location method according to claim 4, wherein, The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating method and device for a power transmission line. The application relates to a fault locating The longest time for the traveling wave signal of the fault point in each line section to reach the traveling wave detection device one and the traveling wave detection device two is respectively: , , ; Based on , determining a line section , then the fault point is located in the line section .

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