Power transmission line foreign matter monitoring system and method based on wind speed regulation laser radar

Abstract

The invention discloses a power transmission line foreign matter monitoring system and method based on a wind speed regulation laser radar, and the system comprises a main control module which is used for issuing a data collection instruction; the three-dimensional laser radar is used for detecting a multi-loop power transmission line and the periphery, obtaining background three-dimensional point cloud data and real-time three-dimensional point cloud data and returning the data to the main control module; the anemograph is used for acquiring real-time wind speed information of a multi-loop power transmission line and the periphery according to a wind speed acquisition instruction issued by the main control module and returning the real-time wind speed information to the main control module; and the background server receives the real-time three-dimensional point cloud data, compares the real-time three-dimensional point cloud data with background three-dimensional point cloud data to generate differential data, performs clustering processing on the differential data to judge whether there is an invading foreign matter, and generates a monitoring report. According to the invention, real-time automatic monitoring and accurate positioning of foreign matters on the power transmission line are realized, danger levels are accurately distinguished, operation and maintenance decisions are assisted, and the operation safety and reliability of a power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line monitoring, and in particular to a transmission line foreign matter monitoring system and method based on wind speed control laser radar. Background Art

[0002] As an important part of the power network, high-voltage transmission lines are widely distributed. These transmission lines will inevitably be disturbed by floating foreign objects such as kites, plastic bags, and films during operation. These foreign objects may cause accidents such as short circuits when they are built on power lines. Therefore, monitoring transmission lines and timely detecting and handling intrusions or overlapping foreign objects are important links to ensure the safe operation of the power system.

[0003] Traditional manual inspection methods are highly subjective, inefficient, and have discontinuous inspection times, with a high risk of missed inspections. The video detection system is highly dependent on light, making it difficult to achieve all-weather monitoring of transmission lines. In this context, radar technology has opened up a new path for automated monitoring of power transmission lines. However, conventional radars have shortcomings in identifying objects with low reflectivity. In comparison, three-dimensional lidar has significant advantages in detecting common overlaps on power lines. However, there are still many challenges in terms of massive data processing and foreign object hazard assessment. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the prior art.

[0005] The technical solution adopted by the present invention to solve the technical problem is: to provide a transmission line foreign body monitoring system based on wind speed control laser radar, comprising:

[0006] The main control module is used to issue data collection instructions, including background 3D point cloud data collection instructions, real-time 3D point cloud data collection instructions and wind speed collection instructions;

[0007] The 3D laser radar detects multiple power transmission lines and their surroundings according to the background 3D point cloud data collection instructions issued by the main control module, obtains background 3D point cloud data and returns it to the main control module; according to the real-time 3D point cloud data collection instructions issued by the main control module, detects multiple power transmission lines and their surroundings, obtains real-time 3D point cloud data and returns it to the main control module;

[0008] The anemometer obtains the real-time wind speed information of multiple power transmission lines and their surroundings according to the wind speed collection instructions issued by the main control module and returns it to the main control module;

[0009] The backend server receives real-time 3D point cloud data and compares it with the background 3D point cloud data to generate differential data, performs clustering processing on the differential data to determine whether there are intruding foreign objects, and generates a monitoring report.

[0010] Preferably, the three-dimensional laser radar has multi-angle, high-resolution scanning capabilities, and its installation angle and position need to be adjusted to ensure coverage of the transmission line range, and the transmission line and surrounding space can be scanned as needed to generate high-density point cloud data.

[0011] Preferably, the main control module adjusts the working mode of the three-dimensional laser radar to collect real-time three-dimensional point cloud data according to the real-time wind speed information, specifically:

[0012] If the wind speed is greater than or equal to a set threshold or a foreign object is detected, the three-dimensional laser radar is adjusted to perform continuous monitoring;

[0013] If the wind speed is less than the set threshold, the three-dimensional laser radar is adjusted to perform regular monitoring.

[0014] Preferably, after the background server determines that there is an intruding foreign object, it also uses a convolutional neural network model to identify and assess the risk of the foreign object.

[0015] Preferably, the method of using a convolutional neural network model to identify and assess the risk of foreign matter comprises the following steps:

[0016] Receive real-time 3D point cloud data and real-time wind speed information;

[0017] Preprocess the real-time 3D point cloud data to obtain voxelized or projected point cloud data, which are input into the convolutional neural network model together with the real-time wind speed information;

[0018] The convolutional neural network model uses an input layer to receive preprocessed point cloud data, a convolution layer to extract the shape and size features of foreign objects, a pooling layer to reduce the dimension and reduce the amount of calculation, a fully connected layer to fuse features and combine them with wind speed-related feature vectors, and an output layer to output prediction results; the prediction results include the type of foreign objects, the distance between the foreign objects and the line, the possibility of contact between the foreign objects and the line, and the displacement trend under the action of wind speed;

[0019] A foreign body hazard assessment is performed based on the prediction results.

[0020] Preferably, for the convolutional neural network model, a large amount of labeled sample data is used for offline training to optimize the model weights and ensure recognition accuracy.

[0021] Preferably, the foreign body hazard assessment based on the prediction result includes the following three methods:

[0022] The danger level is determined based on the distance between the foreign object and the line. When the distance between the foreign object and the line is within 0-2 meters, the danger level is high; when the distance between the foreign object and the line is 2-5 meters, the danger level is medium; when the distance between the foreign object and the line exceeds 5 meters, the danger level is low;

[0023] The possibility of foreign objects contacting the lines is determined. If the foreign objects are connected to multiple transmission lines, the danger level is high; if the foreign objects are connected to a single transmission line, the danger level is medium; if the foreign objects are floating and not touching the transmission line, the danger level is low.

[0024] The judgment is made based on the displacement trend under the action of wind speed. Under the action of wind speed, when the displacement speed of the foreign body is greater than 1 m / s and the direction is directly pointing to the line, the danger level is high; when the displacement speed of the foreign body is 0.1-1 m / s and the direction is pointing to the line, the danger level is medium; when the displacement speed of the foreign body is less than 0.1 m / s or the direction is away from the line, the danger level is low.

[0025] Preferably, it also includes:

[0026] A data storage module, used for storing background 3D point cloud data, real-time 3D point cloud data and real-time wind speed information;

[0027] The communication module is used for communication between the main control module and the backend server; the main control module uses the communication module to send the background 3D point cloud data, real-time 3D point cloud data and real-time wind speed information to the backend server; the backend server uses the communication module to send the detection information to the main control module;

[0028] The power supply module obtains electrical energy through the photovoltaic panel and is connected to the main control module for energy supply.

[0029] Preferably, the main control module also issues an alarm message based on the monitoring report.

[0030] The present invention also provides a method for monitoring foreign objects in a power transmission line based on a wind speed control laser radar, based on any of the above-mentioned systems, comprising the following steps:

[0031] The main control module sends a background 3D point cloud acquisition instruction, and the 3D laser radar detects multiple power transmission lines and their surroundings, obtains background point cloud data and returns it to the main control module;

[0032] The main control module transmits the background point cloud data to the backend server, and the backend server uses the background three-dimensional point cloud data to construct a background three-dimensional point cloud model;

[0033] The main control module sends a wind speed collection command, and the anemometer obtains real-time wind speed information of multiple power transmission lines and the surrounding areas, and returns it to the main control module;

[0034] The main control module controls the working mode of the three-dimensional laser radar to collect real-time three-dimensional point cloud data according to the real-time wind speed information, and issues a real-time three-dimensional point cloud data collection instruction including the working mode;

[0035] The 3D laser radar collects real-time 3D point cloud data and returns it to the main control module;

[0036] The main control module sends real-time 3D point cloud data and real-time wind speed information to the backend server;

[0037] The backend server receives real-time 3D point cloud data, compares it with the background 3D point cloud model, and generates differential data; clusters the differential data to determine whether there are intruding foreign objects.

[0038] The present invention has the following beneficial effects:

[0039] (1) The present invention realizes the on-demand operation of the three-dimensional laser radar by intelligently controlling the wind speed, while also reducing the amount of data to be processed, saving energy, and reducing operating costs;

[0040] (2) The present invention is real-time and dynamic. By combining wind speed information and utilizing the recognition and update function of a convolutional neural network, it can timely capture new or changing foreign objects, obtain a dynamic model of invading foreign objects, and improve the response speed and adaptability of the system.

[0041] (3) The present invention can effectively reduce the false alarm rate. By comparing the real-time three-dimensional point cloud data with the background model, a differential data set is generated, which helps to filter out most of the irrelevant background information, thereby reducing the false alarm rate. The differential data is clustered to form a three-dimensional enclosed area containing potential intruding foreign objects, further improving the accuracy of foreign object recognition.

[0042] (4) The present invention uses a three-dimensional laser radar to provide high-precision three-dimensional point cloud data, making the modeling of the transmission line and its surrounding environment more accurate. Through image processing and feature extraction using a convolutional neural network, potential intrusive foreign objects can be more effectively identified and classified, thereby improving the accuracy of detection. The present invention continuously learns and optimizes the convolutional neural network model, and the system can gradually adapt. The present invention can realize automated monitoring of transmission lines, reduce the need for manual inspections, reduce operation and maintenance costs, and improve work efficiency. The three-dimensional laser radar uses non-contact measurement technology, will not cause any physical damage to the transmission line, and can achieve non-destructive testing.

[0043] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments, but the present invention is not limited to the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a system schematic diagram of an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of a three-dimensional laser radar scanning range according to an embodiment of the present invention;

[0046] Figure 3 A method step diagram of an embodiment of the present invention;

[0047] Figure 4 This is a schematic diagram of the process of wind speed control laser radar according to an embodiment of the present invention;

[0048] Figure 5 The figure is a schematic diagram of a process for judging the danger level of foreign matter according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] See also Figure 1 FIG. 1 is a schematic diagram of a system according to an embodiment of the present invention, comprising

[0050] The main control module is used to issue data collection instructions, including background 3D point cloud data collection instructions, real-time 3D point cloud data collection instructions and wind speed collection instructions;

[0051] The 3D laser radar detects multiple power transmission lines and their surroundings according to the background 3D point cloud data collection instructions issued by the main control module, obtains background 3D point cloud data and returns it to the main control module; according to the real-time 3D point cloud data collection instructions issued by the main control module, detects multiple power transmission lines and their surroundings, obtains real-time 3D point cloud data and returns it to the main control module;

[0052] The anemometer obtains the real-time wind speed information of multiple power transmission lines and their surroundings according to the wind speed collection instructions issued by the main control module and returns it to the main control module;

[0053] The backend server receives the real-time 3D point cloud data and compares it with the background 3D point cloud data to generate differential data, performs clustering processing on the differential data to determine whether there are intruding foreign objects, and generates a monitoring report;

[0054] A data storage module, used for storing background 3D point cloud data, real-time 3D point cloud data and real-time wind speed information;

[0055] The communication module is used for communication between the main control module and the backend server; the main control module uses the communication module to send the background 3D point cloud data, real-time 3D point cloud data and real-time wind speed information to the backend server; the backend server uses the communication module to send the detection information to the main control module;

[0056] The power supply module obtains electrical energy through the photovoltaic panel and is connected to the main control module for energy supply.

[0057] Specifically, the three-dimensional laser radar has multi-angle, high-resolution scanning capabilities. Its installation angle and position refer to Figure 2 As shown, it needs to be adjusted to ensure that the range of the transmission lines is covered, and the transmission lines and surrounding spaces can be scanned as needed to generate high-density point cloud data.

[0058] Specifically, the clustering process for performing clustering on the differential data to determine whether there is an intruding foreign object may adopt a commonly used clustering process method such as a DBSCAN clustering algorithm.

[0059] Specifically, the background server can pre-process the data received from the communication module to obtain voxelized or projected point cloud data, and then build a model based on the convolutional neural network algorithm to identify and assess the danger of foreign objects connected to the transmission lines, and generate a monitoring report at the same time. The monitoring report includes wind speed information around the multiple power transmission lines, point cloud data, foreign object type data and the danger level of the foreign objects; at the same time, the background server includes a data management module and a report set module.

[0060] The convolutional neural network model is divided into five layers: input layer, convolution layer, pooling layer, etc. The input layer is responsible for receiving pre-processed point cloud data, the convolution layer is responsible for extracting features such as shape and size of foreign objects, the pooling layer is responsible for reducing the dimension and reducing the amount of calculation, the fully connected layer is responsible for fusing features and combining with wind speed-related feature vectors, and the output layer is responsible for outputting the foreign object category and the corresponding danger level, including the high danger level of foreign objects connected to multiple transmission lines, the medium danger level of foreign objects connected to a single transmission line, and the danger warning level of foreign objects floating without touching the transmission line. The judgment is made by the distance of the foreign object from the line, the possibility of contact, and the displacement trend under the action of wind speed. A large amount of labeled sample data is used for offline training to optimize the model weights to ensure the recognition accuracy.

[0061] Among them, the data management module is used to screen and classify the foreign object data after the background server generates the peripheral data of the multiple power transmission lines, and add the line foreign object data to the report set; the report set module is used to receive the report set and generate a monitoring report.

[0062] For details, see Figure 3 FIG. 1 is a diagram showing steps of a method according to an embodiment of the present invention, comprising the following steps:

[0063] S301, the main control module sends a background 3D point cloud acquisition instruction, and the 3D laser radar detects multiple power transmission lines and their surroundings, obtains background point cloud data and returns it to the main control module;

[0064] S302, the main control module transmits the background point cloud data to the backend server, and the backend server uses the background three-dimensional point cloud data to construct a background three-dimensional point cloud model;

[0065] S303, the main control module sends a wind speed collection instruction, and the anemometer obtains real-time wind speed information of multiple power transmission lines and the surrounding areas in real time, and returns it to the main control module;

[0066] S304, the main control module controls the working mode of the three-dimensional laser radar to collect real-time three-dimensional point cloud data according to the real-time wind speed information, and issues a real-time three-dimensional point cloud data collection instruction including the working mode;

[0067] S305, the 3D laser radar collects real-time 3D point cloud data and returns it to the main control module;

[0068] S306, the main control module sends the real-time three-dimensional point cloud data and real-time wind speed information to the backend server;

[0069] S307, the backend server receives the real-time 3D point cloud data, compares it with the background 3D point cloud model, generates differential data, and performs clustering processing on the differential data to determine whether there is an intruding foreign object.

[0070] Specifically, in S304, the main control module controls the working mode of the three-dimensional laser radar to collect real-time three-dimensional point cloud data according to the real-time wind speed information. Figure 4 As shown, specifically:

[0071] If the wind speed is greater than or equal to a set threshold, the three-dimensional laser radar is adjusted to perform continuous monitoring;

[0072] If the wind speed is less than the set threshold, the three-dimensional laser radar is adjusted to perform regular monitoring.

[0073] For example: setting the threshold to 6 m / s, if the wind speed is greater than or equal to 6 m / s, the 3D laser radar is adjusted to monitor continuously; if the wind speed is less than 6 m / s, the 3D laser radar is adjusted to monitor once an hour.

[0074] Specifically, in S307, after the background server determines that there is an intruding foreign object, it also uses a convolutional neural network model to identify and assess the danger of the foreign object, including determining the danger level of the foreign object by the distance between the foreign object and the line, the possibility of contact between the foreign object and the line, or the displacement trend under the effect of wind speed, as follows:

[0075] (1) Determine based on the distance between the foreign object and the line, specifically:

[0076] Close-range area: When the distance between the foreign object and the line is within 0-2 meters, the danger level is high;

[0077] Medium distance area: When the distance is 2-5 meters, the danger level is medium;

[0078] Long-distance area: When the distance exceeds 5 meters, the danger level is low.

[0079] (2) Determine based on the possibility of foreign matter contacting the line, see Figure 5 As shown, specifically:

[0080] High contact potential: foreign objects are connected to multiple transmission lines, and the danger level is high;

[0081] Medium contact possibility: foreign objects are connected to a single transmission line, and the danger level is medium;

[0082] Low contact possibility: If foreign objects float and do not touch the power lines, the danger level is low.

[0083] (3) Determine based on the displacement trend under the action of wind speed, specifically:

[0084] Rapidly approaching the line: Under the influence of wind speed, the displacement speed of foreign objects is greater than 1 m / s and the direction is directly pointing to the line, and the danger level is high;

[0085] Slowly approach the line: When the displacement speed of foreign objects under the influence of wind speed is 0.1-1 m / s and the direction is toward the line, the danger level is medium;

[0086] Far away from the line or no obvious displacement trend: Under the action of wind speed, the displacement speed of foreign objects is less than 0.1 m / s or the direction is away from the line, and the danger level is low.

[0087] Specifically, the main control module also issues an alarm message based on the monitoring report so that the inspection personnel can handle it in time.

[0088] The present invention proposes a transmission line foreign object monitoring system and method based on wind speed control laser radar, which realizes real-time automatic monitoring of foreign objects in the transmission line, precise positioning, and accurate distinction of danger levels, assists operation and maintenance decision-making, and improves the safety and reliability of power system operation.

[0089] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A transmission line foreign body monitoring system based on wind speed control laser radar, characterized in that: include: The main control module is used to issue data collection instructions, including background 3D point cloud data collection instructions, real-time 3D point cloud data collection instructions and wind speed collection instructions; The 3D laser radar detects multiple power transmission lines and their surroundings according to the background 3D point cloud data collection instructions issued by the main control module, obtains background 3D point cloud data and returns it to the main control module; according to the real-time 3D point cloud data collection instructions issued by the main control module, detects multiple power transmission lines and their surroundings, obtains real-time 3D point cloud data and returns it to the main control module; The anemometer obtains the real-time wind speed information of multiple power transmission lines and their surroundings according to the wind speed collection instructions issued by the main control module and returns it to the main control module; The backend server receives real-time 3D point cloud data and compares it with the background 3D point cloud data to generate differential data, performs clustering processing on the differential data to determine whether there are intruding foreign objects, and generates a monitoring report.

2. The power transmission line foreign body monitoring system based on wind speed control laser radar according to claim 1 is characterized in that: The three-dimensional laser radar has multi-angle, high-resolution scanning capabilities. Its installation angle and position need to be adjusted to ensure coverage of the transmission line range, and it can scan the transmission lines and surrounding space on demand to generate high-density point cloud data.

3. The power transmission line foreign body monitoring system based on wind speed control laser radar according to claim 1 is characterized in that: The main control module adjusts the working mode of the three-dimensional laser radar to collect real-time three-dimensional point cloud data according to the real-time wind speed information, specifically: If the wind speed is greater than or equal to a set threshold or a foreign object is detected, the three-dimensional laser radar is adjusted to perform continuous monitoring; If the wind speed is less than the set threshold, the three-dimensional laser radar is adjusted to perform regular monitoring.

4. The power transmission line foreign body monitoring system based on wind speed control laser radar according to claim 1 is characterized in that: After the background server determines that there is an intruding foreign object, it also uses a convolutional neural network model to identify and assess the risk of the foreign object.

5. The power transmission line foreign body monitoring system based on wind speed control laser radar according to claim 4 is characterized in that: The method of using a convolutional neural network model to identify and assess the risk of foreign matter includes the following steps: Receive real-time 3D point cloud data and real-time wind speed information; Preprocess the real-time 3D point cloud data to obtain voxelized or projected point cloud data, which are input into the convolutional neural network model together with the real-time wind speed information; The convolutional neural network model uses an input layer to receive preprocessed point cloud data, a convolution layer to extract the shape and size features of foreign objects, a pooling layer to reduce the dimension and reduce the amount of calculation, a fully connected layer to fuse features and combine them with wind speed-related feature vectors, and an output layer to output prediction results; the prediction results include the type of foreign objects, the distance between the foreign objects and the line, the possibility of contact between the foreign objects and the line, and the displacement trend under the action of wind speed; A foreign body hazard assessment is performed based on the prediction results.

6. The power transmission line foreign body monitoring system based on wind speed control laser radar according to claim 5 is characterized in that: For the convolutional neural network model, a large amount of labeled sample data is used for offline training to optimize the model weights and ensure recognition accuracy.

7. The power transmission line foreign body monitoring system based on wind speed control laser radar according to claim 5 is characterized in that: The foreign body hazard assessment based on the prediction result includes the following three methods: The danger level is determined based on the distance between the foreign object and the line. When the distance between the foreign object and the line is within 0-2 meters, the danger level is high; when the distance between the foreign object and the line is 2-5 meters, the danger level is medium; when the distance between the foreign object and the line exceeds 5 meters, the danger level is low; The possibility of foreign objects contacting the lines is determined. If the foreign objects are connected to multiple transmission lines, the danger level is high; if the foreign objects are connected to a single transmission line, the danger level is medium; if the foreign objects are floating and not touching the transmission line, the danger level is low. The judgment is made based on the displacement trend under the action of wind speed. Under the action of wind speed, when the displacement speed of the foreign body is greater than 1 m / s and the direction is directly pointing to the line, the danger level is high; when the displacement speed of the foreign body is 0.1-1 m / s and the direction is pointing to the line, the danger level is medium; when the displacement speed of the foreign body is less than 0.1 m / s or the direction is away from the line, the danger level is low.

8. The power transmission line foreign body monitoring system based on wind speed control laser radar according to claim 1 is characterized in that: Also includes: A data storage module, used for storing background 3D point cloud data, real-time 3D point cloud data and real-time wind speed information; Communication module, used for communication between the main control module and the backend server; The main control module uses the communication module to send the background three-dimensional point cloud data, the real-time three-dimensional point cloud data and the real-time wind speed information to the backend server; the backend server uses the communication module to send the detection information to the main control module; The power supply module obtains electrical energy through the photovoltaic panel and is connected to the main control module for energy supply.

9. The power transmission line foreign body monitoring system based on wind speed control laser radar according to claim 8 is characterized in that: The main control module also issues an alarm message according to the monitoring report.

10. A method for monitoring foreign objects in a power transmission line based on a wind speed control laser radar, based on the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: The main control module sends a background 3D point cloud acquisition instruction, and the 3D laser radar detects multiple power transmission lines and their surroundings, obtains background point cloud data and returns it to the main control module; The main control module transmits the background point cloud data to the backend server, and the backend server uses the background three-dimensional point cloud data to construct a background three-dimensional point cloud model; The main control module sends a wind speed collection command, and the anemometer obtains real-time wind speed information of multiple power transmission lines and the surrounding areas, and returns it to the main control module; The main control module controls the working mode of the three-dimensional laser radar to collect real-time three-dimensional point cloud data according to the real-time wind speed information, and issues a real-time three-dimensional point cloud data collection instruction including the working mode; The 3D laser radar collects real-time 3D point cloud data and returns it to the main control module; The main control module sends real-time 3D point cloud data and real-time wind speed information to the backend server; The backend server receives real-time 3D point cloud data, compares it with the background 3D point cloud model, and generates differential data; Perform clustering on the differential data to determine whether there are intruding foreign objects.

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