A method for detecting power outage faults at distribution network nodes

Abstract

The present application relates to the field of power line carrier communication technology, and specifically to a method for intelligent detection of power outage faults at distribution network nodes, including each node of the distribution network monitoring the reverse LQI and the remaining effective time of the neighbor table of the adjacent node, and sending an abnormal alarm frame to the concentrator when the reverse LQI is lower than a first preset threshold and the remaining effective time of the neighbor table is less than a second preset threshold; the concentrator compares the deviations of the abnormal neighbor table remaining effective time and the abnormal reverse LQI in the parameter abnormal alarm frame, and obtains the comparison result of the deviation of the neighbor table remaining effective time and the comparison result of the reverse LQI deviation. If the comparison result of the deviation of the neighbor table remaining effective time and the comparison result of the reverse LQI deviation are both less than the respective set thresholds, the concentrator determines that a power outage fault occurs at the corresponding abnormal node. This method realizes the detection of power outage faults at distribution network nodes without increasing additional hardware costs, and improves the timeliness and accuracy of detection.

Description

Technical Field

[0001] The present application relates to the technical field of power line carrier communication, and in particular to a method for detecting power outage faults at distribution network nodes. Background Art

[0002] The requirements of modern smart grids for power outage fault detection are mainly reflected in timeliness and accuracy. The development of smart grids aims to achieve reliable, safe, economical, efficient and environmentally friendly power grids, while providing conditions for the grid connection of new energy power generation such as solar energy and wind energy, and promoting the public and enterprises to reduce energy consumption and reduce carbon dioxide emissions. In this context, smart meters, as an important part of smart grids, have special emphasis on the need for power outage fault detection in their design specifications, namely timeliness and accuracy, to ensure that key information for power grid operation and maintenance repairs can be quickly obtained. Among them, timeliness requires that the distribution network can quickly perceive power outage faults and realize closed-loop management of power outage operation and maintenance repair operations, that is, the distribution network needs to transmit information to the power grid operator as soon as possible after the power outage occurs, so as to respond and handle it quickly. Accuracy requires that the power outage fault information detected by the distribution network must be accurate and correct, covering details such as the start and end time of the power outage and the scope of impact, so that the power grid operator can accurately determine the fault location and cause and take measures to restore power supply in a timely manner.

[0003] However, the current power outage fault detection schemes for distribution network nodes based on PLC communication are all to add a large-capacity supercapacitor to the PLC communication circuit. When a power outage occurs in the smart meter, the supercapacitor is used to power the PLC module, so that the PLC module sends the last frame of the power outage reporting frame to the power line. For example, the patent application with the publication number CN108593991A discloses a power outage alarm device based on wireless communication, in which a supercapacitor module and a power outage detection circuit are provided. When a power outage occurs, the supercapacitor is used to power the communication module. After the communication module detects a power outage through the power outage detection circuit, an alarm event is generated and reported, thereby realizing real-time reporting of the power outage alarm event after the smart meter has a power outage. However, although the device realizes real-time alarm reporting after a power outage of the smart meter, it not only increases the cost of a single hardware device in the network, but also has the problem of untimely and inaccurate detection. Summary of the invention

[0004] Based on this, it is necessary to provide a method for detecting power outage faults in distribution network nodes in view of the problems of untimely and inaccurate detection in existing methods for detecting power outage faults in distribution network nodes. The method comprises:

[0005] Step S1, the local node obtains the reverse LQI (Link Quality Indication) of the adjacent node and the remaining valid time of the neighbor table;

[0006] Step S2, when the reverse LQI is less than the first preset threshold and the remaining valid time of the neighbor table is less than the second preset threshold, the local node sends a parameter abnormality alarm frame to the concentrator; the parameter abnormality alarm frame includes the abnormal node address, the remaining valid time of the abnormal neighbor table and the abnormal reverse LQI;

[0007] Step S3, the concentrator compares the deviations of the remaining effective time of the abnormal neighbor table and the abnormal reverse LQI in the parameter abnormality alarm frame of the same abnormal node address, and obtains the comparison result of the remaining effective time deviation of the neighbor table and the comparison result of the reverse LQI deviation;

[0008] Step S4: When the comparison result of the remaining effective time deviation of the neighbor table is less than the first deviation comparison threshold, and the comparison result of the reverse LQI deviation is less than the second deviation comparison threshold, the concentrator determines that a power outage occurs in the node corresponding to the abnormal node address.

[0009] In one embodiment, step S3 comprises:

[0010] Step S31, the concentrator parses the parameter abnormality alarm frame to obtain the abnormal node address, and counts the number of parameter abnormality alarm frames with the same abnormal node address;

[0011] Step S32, when the number of parameter abnormality alarm frames with the same abnormal node address is greater than the first number threshold, the concentrator compares the deviations of the remaining valid time of the abnormal neighbor table and the abnormal reverse LQI in the parameter abnormality alarm frames with the same abnormal node address, and obtains the comparison result of the remaining valid time deviation of the neighbor table and the comparison result of the reverse LQI deviation.

[0012] In one embodiment, the parameter abnormality alarm frame further includes a source node address, and after step S2 and before step S3, the following is further included:

[0013] Step S21: the concentrator classifies the parameter abnormality alarm frames according to the source node addresses.

[0014] In one embodiment, the reverse LQI is calculated as:

[0015] EMA(LQI(t))=α*X(LQI(t))+(1-α)*EMA(LQI(t-1))

[0016] Where EMA(LQI(t)) represents the exponentially weighted moving average of the LQI parameter at time t, X(LQI(t)) represents the LQI parameter value at time t, EMA(LQI(t-1)) represents the exponentially weighted moving average of the LQI parameter at time t-1, and α is the smoothing parameter. The calculation formula of the LQI parameter is:

[0017] LQI=(SNR+10)*255 / (53.75+10)=(SNR+10)*4

[0018] Where SNR is the signal-to-noise ratio of the communication link.

[0019] In one embodiment, the value of the smoothing parameter is 1 / 8.

[0020] In one embodiment, step S1 comprises:

[0021] Step S10, the local node obtains the neighbor table of the adjacent node;

[0022] Step S11, generating an information table according to the short address in the neighbor table, the reverse LQI and the remaining valid time of the neighbor table.

[0023] In one embodiment, the method for detecting power outage faults at distribution network nodes further includes:

[0024] Step S12: The local node periodically checks every minute whether the reverse LQI in the information table is less than a first preset threshold, and whether the remaining valid time of the neighbor table is less than a second preset threshold.

[0025] In one embodiment, the first preset threshold is 100, and the second preset threshold is 50.

[0026] In one embodiment, the first deviation comparison threshold is 10, and the second deviation comparison threshold is 5.

[0027] In one of the embodiments, the parameter abnormality alarm frame also includes an object identification ID number.

[0028] The above-mentioned distribution network node power outage fault detection method includes each node in the distribution network monitoring the reverse LQI parameter and the remaining effective time parameter of the neighbor table in the adjacent node, and sending a parameter abnormality alarm frame to the concentrator when the reverse LQI parameter is less than a first preset threshold and the remaining effective time of the neighbor table is less than a second preset threshold. The concentrator receives and compares the deviation of the abnormal neighbor table remaining effective time and the abnormal reverse LQI in the alarm frame with the same abnormal node address, and obtains the neighbor table remaining effective time deviation comparison result and the reverse LQI deviation comparison result respectively. When the neighbor table remaining effective time deviation comparison result is less than the first deviation comparison threshold, and the reverse LQI deviation comparison result is less than the second deviation comparison threshold, it is judged that the node corresponding to the abnormal node address has a power outage fault. The method can realize intelligent identification of node power outages without increasing additional hardware costs, thereby significantly improving the accuracy and timeliness of power outage detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1A schematic diagram of a flow chart of a method for detecting power outage faults in a distribution network node in an embodiment;

[0030] Figure 2 A schematic diagram of a flow chart of a method for detecting power outage faults in a distribution network node in another embodiment;

[0031] Figure 3 It is a flowchart of a method for detecting power outage faults in a distribution network node in another embodiment;

[0032] Figure 4 The schematic diagram is a representation of information of neighboring nodes of a normal node in one embodiment;

[0033] Figure 5 The figure is a schematic diagram showing information of neighboring nodes of an abnormal node in one embodiment. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0035] Example 1

[0036] like Figure 1 As shown, this embodiment provides a method for detecting power outage faults at a distribution network node, including:

[0037] Step S1: The local node obtains the reverse LQI of the adjacent node and the remaining validity time of the neighbor table.

[0038] Among them, the two parameters of reverse LQI and the remaining effective time of the neighbor table can reflect the communication capability of the node. The larger the value of reverse LQI and the remaining effective time of the neighbor table, the stronger the communication capability of the node, and vice versa. The smaller the value, the weaker the communication capability. The reverse LQI (Link Quality Indication) parameter Reverse_LQI is the link quality indicator of the link from the node to the neighbor, with a size of 8 bits. The calculation formula of reverse LQI is shown in formula (1):

[0039] EMA(LQI(t))=α*X(LQI(t))+(1-α)*EMA(LQI(t-1)) (1)

[0040] Wherein, EMA(LQI(t)) represents the exponentially weighted moving average of the LQI parameter at time t, X(LQI(t)) represents the LQI parameter value at time t, EMA(LQI(t-1)) represents the exponentially weighted moving average of the LQI parameter at time t-1, α is a smoothing parameter, and the value of the smoothing parameter can be set by those skilled in the art according to specific application scenarios and requirements. Preferably, the smoothing parameter α=1 / 8.

[0041] The calculation formula of the LQI parameter in formula (1) is shown in formula (2):

[0042] LQI=(SNR+10)*255 / (53.75+10)=(SNR+10)*4 (2)

[0043] Where SNR is the signal-to-noise ratio of the communication link.

[0044] Neighbor table remaining valid time Neighbor_valid_time is used to indicate the remaining valid time of the neighbor table. The maximum value of this parameter is set to 255 seconds, and it will automatically decrease every 4 seconds. In addition, when the concentrator sends a unicast to a node in the distribution network, the concentrator will require the node to send an ACK confirmation. If the node fails to send an ACK confirmation and the reason for the failure is not that the channel is busy, the remaining valid time of the neighbor table of the node will be immediately cleared.

[0045] Specifically, in the distribution network, each node is equipped with a neighbor table. By accessing the neighbor table of the neighbor node, the local node can obtain the reverse LQI of these neighbor nodes and the remaining validity time of the neighbor table. In some embodiments, the specific definition of the neighbor table is shown in Table 1.

[0046] Table 1

[0047]

[0048] Step S2: When the reverse LQI is less than the first preset threshold and the remaining valid time of the neighbor table is less than the second preset threshold, the local node sends a parameter abnormality alarm frame to the concentrator. The parameter abnormality alarm frame includes the abnormal node address, the remaining valid time of the abnormal neighbor table and the abnormal reverse LQI.

[0049] Specifically, after obtaining the reverse LQI of the adjacent node and the remaining effective time of the neighbor table, the local node checks whether the reverse LQI of each adjacent node is less than the set first preset threshold, and at the same time determines whether the remaining effective time of the neighbor table is less than the set second preset threshold. When it is found that the reverse LQI of a certain adjacent node is greater than or equal to the first preset threshold, and the remaining effective time of its neighbor table is also greater than or equal to the second preset threshold, the adjacent node will be regarded as an abnormal node. At this time, the local node will record the short address of the abnormal node, the remaining effective time of the neighbor table, and the reverse LQI, and combine this information into a parameter abnormality alarm frame and send it to the concentrator. Preferably, the first preset threshold is 100 and the second preset threshold is 50.

[0050] Further, after the local node obtains the neighbor table of the adjacent node, an information table as shown in Table 2 can be generated according to the short address, reverse LQI and remaining valid time of the neighbor table in the neighbor table. The local node periodically checks whether the reverse LQI in the information table is less than the first preset threshold value and whether the remaining valid time of the neighbor table is less than the second preset threshold value at a preset time interval. Preferably, the local node periodically checks whether the reverse LQI in the information table is less than the first preset threshold value and whether the remaining valid time of the neighbor table is less than the second preset threshold value every minute.

[0051] Table 2

[0052]

[0053] Step S3, the concentrator compares the deviations of the remaining valid time of the abnormal neighbor table and the abnormal reverse LQI in the parameter abnormality alarm frame of the same abnormal node address, and obtains the comparison result of the remaining valid time deviation of the neighbor table and the comparison result of the reverse LQI deviation.

[0054] Specifically, after the concentrator obtains the parameter abnormality alarm frames from different nodes, it first screens out the parameter abnormality alarm frames with the same abnormal node address, and then parses out the remaining effective time of the abnormal neighbor table in each parameter abnormality alarm frame with the same abnormal node address, compares these remaining effective times of the abnormal neighbor table in pairs, calculates the difference between them, and then uses the absolute value of the difference as the comparison result of the deviation of the neighbor table remaining effective time. Similarly, the concentrator parses out the abnormal reverse LQI in each parameter abnormality alarm frame with the same abnormal node address, compares these abnormal reverse LQI in pairs, calculates the difference between them, and then uses the absolute value of the difference as the comparison result of the deviation of the abnormal reverse LQI.

[0055] Step S4: When the comparison result of the remaining effective time deviation of the neighbor table is less than the first deviation comparison threshold, and the comparison result of the reverse LQI deviation is less than the second deviation comparison threshold, the concentrator determines that a power outage occurs in the node corresponding to the abnormal node address.

[0056] The first deviation comparison threshold and the second deviation comparison threshold can be set by those skilled in the art based on specific application scenarios and usage requirements. Preferably, the first deviation comparison threshold can be 10, and the second deviation comparison threshold can be 5; at this time, the deviation comparison process of step S4 can be expressed by formulas (3)-(4):

[0057] |Neighbor_valid_timeNT1- Neighbor_valid_timeNT2|<10 (3)

[0058] |Reverse LQI1- Reverse LQI2|<5 (4)

[0059] Wherein, Neighbor_valid_timeNT1 represents the remaining valid time of the abnormal neighbor table in the first parameter abnormal alarm frame, Neighbor_valid_timeNT2 represents the remaining valid time of the abnormal neighbor table in the second parameter abnormal alarm frame, Reverse LQI1 represents the abnormal reverse LQI in the first parameter abnormal alarm frame, and Reverse LQI2 represents the abnormal reverse LQI in the second parameter abnormal alarm frame.

[0060] The distribution network node power outage fault detection method in this embodiment includes each node in the distribution network monitoring the reverse LQI parameter and the neighbor table remaining effective time parameter in the adjacent node, and sending a parameter abnormality alarm frame to the concentrator when the reverse LQI parameter is less than a first preset threshold and the neighbor table remaining effective time is less than a second preset threshold. The concentrator receives and compares the deviation of the abnormal neighbor table remaining effective time and the abnormal reverse LQI in the alarm frame with the same abnormal node address, and obtains the neighbor table remaining effective time deviation comparison result and the reverse LQI deviation comparison result respectively. When the neighbor table remaining effective time deviation comparison result is less than the first deviation comparison threshold, and the reverse LQI deviation comparison result is less than the second deviation comparison threshold, it is judged that the node corresponding to the abnormal node address has a power outage fault. The method can realize intelligent identification of node power outages without increasing additional hardware costs, thereby significantly improving the accuracy and timeliness of power outage detection.

[0061] Example 2

[0062] The difference from Example 1 is that Figure 2 As shown, step S3 includes:

[0063] Step S31, the concentrator parses the parameter abnormality alarm frame to obtain the abnormal node address, and counts the number of parameter abnormality alarm frames with the same abnormal node address, wherein the abnormal node address is the short address of the abnormal node.

[0064] Step S32, when the number of parameter abnormality alarm frames with the same abnormal node address is greater than the first number threshold, the concentrator compares the deviations of the abnormal neighbor table remaining valid time and the abnormal reverse LQI in the parameter abnormality alarm frames with the same abnormal node address, and obtains the neighbor table remaining valid time deviation comparison result and the reverse LQI deviation comparison result.

[0065] Specifically, when the concentrator finds that the number of parameter abnormality alarm frames with the same abnormal node address exceeds a preset first quantity threshold, it is considered that the abnormal node may have a real fault. At this time, the concentrator will further compare the deviations of the remaining effective time of the abnormal neighbor table and the abnormal reverse LQI in these parameter abnormality alarm frames. By comparison, the concentrator can obtain the deviation comparison results of the remaining effective time of the neighbor table and the deviation comparison results of the reverse LQI, so as to more accurately determine whether the node has a power outage. Preferably, the first quantity threshold is 1.

[0066] The distribution network node power outage fault detection method in this embodiment can confirm whether the abnormality of a certain node is prevalent and reduce the possibility of false alarms, thereby further improving the accuracy and reliability of node power outage detection by comparing the deviations of the remaining valid time of the abnormal neighbor table and the abnormal reverse LQI in the parameter abnormality alarm frames with the same abnormal node address when the number of parameter abnormality alarm frames with the same abnormal node address is greater than the first number threshold.

[0067] Example 3

[0068] The difference from Example 2 is that the parameter abnormality alarm frame of this embodiment also includes a source node address. The source node address is the short address of the node that sends the parameter abnormality alarm frame. Figure 3 As shown, after step S2 and before step S3, the following further comprises:

[0069] Step S21: the concentrator classifies the parameter abnormality alarm frames according to the source node addresses.

[0070] Specifically, after the concentrator obtains the parameter abnormality alarm frame sent from each node, it first parses the source node address in each parameter abnormality alarm frame, and classifies the parameter abnormality alarm frames with the same source node address into one category; then parses the parameter abnormality alarm frames with the same source node address to obtain the abnormal node address; then counts the number of parameter abnormality alarm frames with the same abnormal node address, and when the number of parameter abnormality alarm frames with the same abnormal node address is greater than the first number threshold, the concentrator compares the deviations of the remaining valid time of the abnormal neighbor table and the abnormal reverse LQI in the parameter abnormality alarm frame with the same abnormal node address, and obtains the comparison result of the deviation of the remaining valid time of the neighbor table and the comparison result of the reverse LQI deviation. When the comparison result of the deviation of the remaining valid time of the neighbor table is less than the first deviation comparison threshold, and the comparison result of the reverse LQI deviation is less than the second deviation comparison threshold, the concentrator determines that the node corresponding to the abnormal node address has a power outage.

[0071] Before classification, the parameter abnormality alarm frames received by the concentrator are disordered, and the parameter abnormality alarm frames from different source nodes are mixed together, which causes the concentrator to process each alarm frame one by one to determine the abnormal node, and cannot quickly identify which nodes have problems, thus reducing processing efficiency. After classification, each parameter abnormality alarm frame is orderly classified according to the source node address, so that the concentrator can quickly lock the abnormal node that is alarmed by multiple nodes at the same time, and then implement targeted processing and analysis, which significantly improves work efficiency.

[0072] Furthermore, the parameter abnormality alarm frame may also include an object identification ID number. The object identification ID number is used to uniquely identify each node in the distribution network, and may be a specific number or character combination. The object identification ID number in the parameter abnormality alarm frame is the object identification ID number of the abnormal node. Specifically, the definition of the parameter abnormality alarm frame is as follows:

[0073]

[0074] The distribution network node power outage fault detection method in this embodiment can quickly identify which nodes have problems by adding the source node address in the parameter abnormality alarm frame, so as to carry out targeted fault troubleshooting and processing, greatly improving the efficiency of power outage fault diagnosis; in addition, the method can quickly and accurately identify abnormal equipment and facilitate maintenance by setting the object identification ID number in the parameter abnormality alarm frame.

[0075] In order to better understand the power distribution network node power outage fault detection method in the above embodiment, a detailed embodiment including specific data is provided below for explanation.

[0076] Example 4

[0077] In this embodiment, the distribution network is composed of multiple PAN (Personal Area Network) networks, each PAN network includes multiple G3-PLC communication nodes and a PAN network concentrator (PAN Coordinator). G3-PLC is a type of NB-PLC (Narrow-Band Power Line Communications) for communication between nodes. Each G3-PLC communication node is configured with a neighbor table as shown in Table 1, and they can access the neighbor table information of adjacent nodes. Based on this information, each node constructs a surrounding neighbor node information table containing the short address of the adjacent node, the reverse LQI, and the remaining valid time of the neighbor table, as shown in Table 2.

[0078] For example, taking a PAN network including 10 G3-PLC communication nodes and a PAN network concentrator as an example, when node 2 in the network does not experience a power outage, the information table of its neighboring nodes (node ​​1, node 3, and node 6) is as follows: Figure 4 As shown in the figure. From the information table of node 2's neighboring nodes, it can be seen that the value of Neighbor_valid_time of each node is greater than 100, and the value of Reverse LQI is greater than 50. At this time, node 1, node 3, and node 6 will not generate parameter abnormality alarm frames. However, when node 2 has a power outage, the information table of its neighboring nodes, namely node 1, node 3, and node 6, is as follows: Figure 5 As shown in the figure, in the information tables of node 1, node 3 and node 6, the values ​​of Reverse LQI of node 2 are all less than 50, and the values ​​of Neighbor_valid_time are all less than 100. At this time, node 1, node 3 and node 6 all send parameter abnormality alarm frames to the PAN network concentrator.

[0079] After receiving the parameter abnormality alarm frames sent by node 1, node 3 and node 6, the PAN network concentrator parses out the abnormal node short address in each parameter abnormality alarm frame, i.e. 0002, the abnormal neighbor table remaining valid time, i.e. 96, 98 and 99, and the abnormal reverse LQI, i.e. 5, 7 and 8. Then, the PAN network concentrator performs deviation comparison on the parsed abnormal neighbor table remaining valid time and abnormal reverse LQI. For the abnormal neighbor table remaining valid time, the PAN network concentrator makes a difference between the two and takes the absolute value to obtain the deviation comparison result of the neighbor table remaining valid time, i.e. 2, 3 and 1. Similarly, for the abnormal reverse LQI, the PAN network concentrator also performs a deviation comparison by making a difference between the two and taking the absolute value to obtain the reverse LQI deviation comparison result, i.e. 2, 3 and 1. Since the deviation comparison results of the neighbor table remaining valid time are all less than 10 and the reverse LQI deviation comparison results are all less than 5, it can be judged that node 2 has a power outage.

[0080] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0081] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for detecting power outage faults at distribution network nodes, characterized in that: The method comprises: Step S1, the local node obtains the reverse LQI of the adjacent node and the remaining validity time of the neighbor table; Step S2, when the reverse LQI is less than a first preset threshold and the remaining valid time of the neighbor table is less than a second preset threshold, the local node sends a parameter abnormality alarm frame to the concentrator; the parameter abnormality alarm frame includes the abnormal node address, the remaining valid time of the abnormal neighbor table and the abnormal reverse LQI; Step S3, the concentrator compares the deviations of the remaining valid time of the abnormal neighbor table and the abnormal reverse LQI in the parameter abnormality alarm frame of the same abnormal node address, and obtains the comparison result of the remaining valid time deviation of the neighbor table and the comparison result of the reverse LQI deviation; Step S4, when the comparison result of the remaining effective time deviation of the neighbor table is less than the first deviation comparison threshold, and the comparison result of the reverse LQI deviation is less than the second deviation comparison threshold, the concentrator determines that the node corresponding to the abnormal node address has a power outage fault; The step S3 comprises: Step S31, the concentrator parses the parameter abnormality alarm frame to obtain the abnormal node address, and counts the number of parameter abnormality alarm frames with the same abnormal node address; Step S32, when the number of the parameter abnormality alarm frames with the same abnormal node address is greater than the first number threshold, the concentrator performs deviation comparison on the remaining valid time of the abnormal neighbor table and the abnormal reverse LQI in the parameter abnormality alarm frames with the same abnormal node address, and obtains the comparison result of the remaining valid time deviation of the neighbor table and the comparison result of the reverse LQI deviation; The calculation formula of the reverse LQI is: EMA(LQI(t))=α*X(LQI(t))+(1-α)*EMA(LQI(t-1)) Where EMA(LQI(t)) represents the exponentially weighted moving average of the LQI parameter at time t, X(LQI(t)) represents the LQI parameter value at time t, EMA(LQI(t-1)) represents the exponentially weighted moving average of the LQI parameter at time t-1, and α is the smoothing parameter. The calculation formula of the LQI parameter is: LQI=(SNR+10)*255 / (53.75+10)=(SNR+10)*4 Where SNR is the signal-to-noise ratio of the communication link.

2. The method for detecting power outage at a distribution network node according to claim 1, characterized in that: The parameter abnormality alarm frame also includes a source node address, and after step S2 and before step S3, it also includes: Step S21: the concentrator classifies the parameter abnormality alarm frames according to the source node addresses.

3. The method for detecting power outage at a distribution network node according to claim 1, characterized in that: The value of the smoothing parameter is 1 / 8.

4. The method for detecting power outage at a distribution network node according to claim 1, characterized in that: The step S1 comprises: Step S10, the local node obtains the neighbor table of the adjacent node; Step S11, generating an information table according to the short address in the neighbor table, the reverse LQI and the remaining valid time of the neighbor table.

5. The method for detecting power outage at a distribution network node according to claim 4, characterized in that: The method further comprises: Step S12: The local node periodically checks every minute whether the reverse LQI in the information table is less than the first preset threshold, and whether the remaining valid time of the neighbor table is less than the second preset threshold.

6. The method for detecting power outage faults at distribution network nodes according to any one of claims 1 or 5, characterized in that: The first preset threshold is 100, and the second preset threshold is 50.

7. The method for detecting power outage at a distribution network node according to claim 1, characterized in that: The first deviation comparison threshold is 10, and the second deviation comparison threshold is 5.

8. The method for detecting power outage at a distribution network node according to claim 1, characterized in that: The parameter abnormality alarm frame also includes an object identification ID number.

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