Sag monitoring method based on edge calculation

Abstract

The invention discloses a sag monitoring method based on edge computing, which comprises the following steps of: deploying a mobile station monitoring device at a key node of a power transmission line to acquire environmental parameters, deploying an edge computing node at a monitoring point to be responsible for local data processing, and setting a background central server to receive, analyze and store a computing result. According to the sag monitoring algorithm implementation method based on edge calculation provided by the invention, the real-time monitoring and early warning of the sag of the power transmission line are realized by deploying edge calculation nodes and combining an efficient algorithm formula, the data transmission delay is reduced, the calculation efficiency is improved, and the accuracy of a monitoring result is ensured.

Description

Technical Field

[0001] The present invention relates to the field of power technology, and particularly to a sag monitoring method based on edge computing. Background Art

[0002] The sag of a transmission line is the amount of droop of the conductor between two points, and its size is directly related to the safe operation of the line. Traditional sag monitoring methods rely on centralized data processing and have many drawbacks. On the one hand, the data transmission delay is large. The data collected from the monitoring points needs to be transmitted to the central server for processing. Long-distance transmission and network congestion are likely to cause untimely data processing and cannot reflect the sag state in real time. On the other hand, the computational resource consumption is high. A large amount of data is processed centrally on the central server, which requires extremely high server performance and increases the operating cost. In addition, the real-time performance is poor and it is difficult to meet the requirement of timely response to sag changes.

[0003] With the development of edge computing technology, sinking computing tasks to network edge nodes has become an effective way to solve the above problems. However, there is a lack of an efficient and accurate algorithm implementation method in the prior art, which cannot give full play to the advantages of edge computing and realize real-time monitoring and early warning of sag. Summary of the Invention

[0004] Embodiments of the present application are proposed to make up for the deficiencies of the prior art and provide a sag monitoring method based on edge computing to solve the problems existing in the prior art.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] A sag monitoring method based on edge computing includes the following steps:

[0007] Deploy a mobile station monitoring device at key nodes of the transmission line to collect environmental parameters, deploy edge computing nodes at the monitoring points to be responsible for local data processing, and set up a background central server to receive the calculation results and analyze and store them;

[0008] Perform data preprocessing on the raw data collected by the sensor based on the Kalman filter algorithm;

[0009] Calculate the actual sag according to the initial sag of the conductor, the initial elevation and the actual elevation;

[0010] When the sag value exceeds the preset threshold, the edge computing node triggers an early warning mechanism;

[0011] The edge computing node transmits the calculated sag value to the background central server through a wireless network, and a compression algorithm is used during transmission to reduce bandwidth occupancy.

[0012] As a further technical solution of the present invention: The formula of the Kalman filter algorithm is:

[0013]

[0014] Among them, is the current state estimation value, is the previous state estimation value, and K k is the Kalman gain, and z k is the current observation value, is the observation matrix;

[0015] The Kalman gain K k is calculated by the following formula:

[0016]

[0017] Among them, P k-1 is the estimation error covariance of the previous state, and R is the observation noise covariance.

[0018] As a further technical solution of the present invention: the formula for calculating the Kalman gain K k is as follows:

[0019]

[0020] Among them, P k-1 is the estimation error covariance of the previous state, and R is the observation noise covariance.

[0021] As a further technical solution of the present invention: the formula for calculating the actual sag f is:

[0022] f = f0 + (h0 - h1)

[0023] Among them, f0 is the initial sag, h0 is the initial elevation, and h1 is the actual elevation.

[0024] As a further technical solution of the present invention: the warning condition is S > St, where St is a preset sag threshold.

[0025] As a further technical solution of the present invention: a distributed computing architecture is adopted to disperse the computing tasks to multiple edge computing nodes, reducing the computing burden on the central server.

[0026] As a further technical solution of the present invention: the formula of the compression algorithm is:

[0027]

[0028] Among them, X is the original data, u is the mean value, and a is the standard deviation.

[0029] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0030] 1. High real-time performance: With the help of terminal edge computing, data transmission latency and data volume are reduced, enabling real-time and effective monitoring of sag, and abnormal sag changes can be detected in a timely manner.

[0031] 2. High computing efficiency: The adopted algorithm formula is simple and efficient, and can be quickly completed on edge computing nodes to meet the computing requirements of real-time monitoring.

[0032] 3. Low resource consumption: Edge computing nodes only process local data, significantly reducing the computing burden on the central server, reducing the dependence on the hardware resources of the central server, and lowering the operating costs.

[0033] 4. Strong reliability: Distributed edge computing nodes improve the fault tolerance of the system. When a certain node fails, other nodes can still work normally to ensure the stable operation of the system.

[0034] 5. Good scalability: The system can flexibly add or reduce edge computing nodes according to actual needs, facilitating applications in power transmission line monitoring scenarios of different scales. Description of the Drawings

[0035] Figure 1 is the schematic diagram of the present invention. Detailed Embodiment

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0037] As Figure 1 shown, a method for implementing a sag monitoring algorithm based on edge computing realizes real-time monitoring and early warning of the sag of a power transmission line by deploying edge computing nodes and combining an efficient algorithm formula. This method can significantly reduce data transmission latency, improve computing efficiency, and ensure the accuracy of monitoring results;

[0038] 1. Overall architecture:

[0039] Deploy mobile station monitoring devices at key nodes of the power transmission line to collect environmental parameters such as the fuzzy coordinate position, temperature, wind speed, and icing photos of the conductor in real time.

[0040] Deploy edge computing nodes at each monitoring point, which are responsible for local data collection, preprocessing, and sag calculation.

[0041] The background central server is used to receive the calculation results of the edge computing nodes and perform data storage and further analysis.

[0042] 2. Algorithm implementation:

[0043] 1) Data preprocessing:

[0044] Filter and denoise the raw data collected by the sensor to ensure data quality. The Kalman filter algorithm is used to smooth the data, and the formula is as follows:

[0045]

[0046] Among them, is the current state estimate value, is the previous state estimate value, K k is the Kalman gain, z k is the current observation value, K k is the observation matrix. The calculation formula of the Kalman gain K k is:

[0047]

[0048] Among them, P k-1 is the estimated error covariance of the previous state, and R is the observation noise covariance.

[0049] 2) Sag calculation:

[0050] Given the initial sag f0 of the wire and recording the initial elevation h0 of the lowest point of the sag (monitoring device) at the initial moment. When the sag changes later, the monitoring device outputs the actual elevation as h1, then h0 - h1 is the elevation difference (i.e., the sag change value). Adding the initial sag f0, the actual sag f of the wire at this time can be obtained. The formula is: f = f0 + (h0 - h1).

[0051] 3) Early warning mechanism:

[0052] When the sag value exceeds the threshold, the edge computing node immediately triggers the early warning mechanism to notify the operation and maintenance personnel for processing. The early warning condition can be expressed as:

[0053] S > St

[0054] Among them, St is the preset sag threshold. The early warning signal is generated through the following logical judgment.

[0055] 3. Data transmission and storage:

[0056] The edge computing node transmits the calculated sag value to the background central server through the wireless network. The data transmission uses a compression algorithm to reduce bandwidth occupancy. The compression formula is:

[0057]

[0058] Among them, X is the original data, u is the mean value, and a is the standard deviation.

[0059] The central server stores and analyzes the received data and generates a historical data report.

[0060] 4. System optimization:

[0061] Adopt a distributed computing architecture to disperse the computing tasks to multiple edge computing nodes, reducing the computing burden on the central server.

[0062] Reduce the data transmission volume through a compression algorithm to reduce the network bandwidth occupancy.

[0063] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention.

[0064] In addition, it should be understood that although this specification is described according to the embodiments, not every embodiment only contains an independent technical solution. This narrative way of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment have been appropriately combined to form other embodiments that are easy for those skilled in the art to understand.

Claims

1. An overhead line sag monitoring method based on edge computing, characterized in that: It includes the following steps: Deploy mobile station monitoring devices at key nodes of the transmission line to collect environmental parameters, deploy edge computing nodes at the monitoring points to be responsible for local data processing, and set up a background central server to receive the calculation results and analyze and store them; Perform data preprocessing on the raw data collected by the sensors based on the Kalman filtering algorithm; Calculate the actual sag according to the initial sag, initial elevation and actual elevation of the wire; When the sag value exceeds the preset threshold, the edge computing node triggers an early warning mechanism; The edge computing node transmits the calculated sag value to the background central server through a wireless network, and a compression algorithm is used during transmission to reduce bandwidth occupancy.

2. The sag monitoring method for edge computing according to claim 1, wherein The formula of the Kalman filtering algorithm is: Among them, is the current state estimation value, is the previous state estimation value, and K k is the Kalman gain, and z k is the current observation value, is the observation matrix; Kalman gain K k The calculation formula is as follows: where P k-1 is the estimated error covariance of the previous state, and R is the observation noise covariance.

3. The sag monitoring method for edge computing according to claim 1, characterized in that The Kalman gain K k is calculated by the following formula: where P k-1 is the estimated error covariance of the previous state, and R is the observation noise covariance.

4. The sag monitoring method for edge computing according to claim 1, characterized in that The calculation formula of the actual sag f is: f = f0 + (h0 - h1) Where f0 is the initial sag, h0 is the initial elevation, and h1 is the actual elevation.

5. The sag monitoring method for edge computing according to claim 1, characterized in that The early warning condition is S > St, where St is the preset sag threshold.

6. The sag monitoring method for edge computing according to claim 1, characterized in that Adopt a distributed computing architecture to disperse the computing tasks to multiple edge computing nodes to reduce the computing burden on the central server.

7. The sag monitoring method for edge computing according to claim 1, wherein The formula of the compression algorithm is: Where X is the raw data, u is the mean value, and a is the standard deviation.

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