A kind of isolating switch closing state monitoring system suitable for dense smoke environment

Abstract

The application provides a kind of disconnecting switch closing state monitoring system suitable for dense smoke environment, including laser radar fixed on ground, smoke sensor fixed on disconnecting switch base, point cloud image data processing module and terminal management platform of host computer. The application accurately distinguishes the closing state of disconnecting switch through the point cloud image data processing method for disconnecting switch, obtains the smoke thickness of disconnecting switch working environment through sensor and sets the experience function to fit the influence of smoke thickness on smoke interference signal pulse width, judges the error rate of monitoring system according to smoke interference signal pulse width, improves the discrimination accuracy of monitoring system, has strong anti-interference ability and wide applicability.

Description

Technical Field

[0001] This invention relates to the field of disconnector switch status detection technology, and more specifically, to a disconnector switch closing status monitoring system suitable for environments with dense smoke. Background Technology

[0002] Disconnecting switches, which isolate voltage through the contact and separation of moving and stationary contacts, are crucial equipment in power systems. Due to their long-term outdoor exposure and environmental influences, they may jam during opening and closing, leading to poor contact between the moving and stationary contacts and potentially causing safety accidents. Therefore, it is necessary to monitor the closing status of disconnecting switches.

[0003] To address the aforementioned issues, while traditional camera-based detection systems can monitor the closing status of disconnect switches to some extent, their performance is significantly reduced in dense smoke environments, and their qualitative judgment methods are prone to false alarms. LiDAR-based monitoring systems are also affected by the working environment, and their accuracy is limited in dense smoke environments. Therefore, further evaluation of the accuracy of the monitoring system's results is necessary. Summary of the Invention

[0004] The technical problem to be solved by this invention is to propose a monitoring system for the closing status of disconnecting switches in dense smoke environments. This system uses lidar to capture three-dimensional point cloud images of the disconnecting switch, accurately determines the closing status of the disconnecting switch through point cloud image data processing methods, obtains the smoke thickness of the working environment of the disconnecting switch through sensors, and sets an empirical function to fit the influence of smoke thickness on the pulse width of the smoke interference signal. The error rate of the monitoring system is determined based on the pulse width of the smoke interference signal, which improves the discrimination accuracy of the monitoring system, has strong anti-interference ability, and has wide applicability.

[0005] The technical solution adopted in this invention is as follows: Design 1. A monitoring system for the closing status of a disconnecting switch suitable for dense smoke environments. The system adopts a laser radar-based device for monitoring the closing status of a disconnecting switch. The device includes a laser radar (1), a disconnecting switch (2), a smoke concentration sensor (3), a slide rail (4), a network cable (5), and a host computer (6) equipped with a point cloud image data processing module (7) and a terminal management platform (8). The laser radar (1) is fixed on the ground by the slide rail (4) and is used to acquire a three-dimensional image of the disconnecting switch (2) when it is closed and save it in the form of point cloud data. The image is transmitted through the network cable (5). The point cloud data is transmitted to the point cloud image data processing module (7) of the host computer; the smoke concentration sensor (3) monitors the smoke thickness value z under the working condition in real time and transmits the value to the terminal management platform (8) of the host computer through the WIFI module; the point cloud image data processing module (7) of the host computer performs visualization, region of interest (ROI) extraction and other processing on the collected point cloud data to complete the extraction of the closing status features of the disconnect switch; the terminal management platform (8) of the host computer displays the closing status of the disconnect switch and the monitoring error rate of the system on the interface.

[0006] This invention provides a method for processing point cloud image data of disconnect switches. The method includes the following steps:

[0007] Step 1: Use a KD-tree-based search algorithm to obtain the point cloud within the Region of Interest (ROI) of the target isolating switch from the input point cloud data;

[0008] Step 2: Using an image target cropping algorithm, a shrinkage factor τ is introduced in the x, y, and z axes respectively. x τ y τ z By changing the shrinkage factor value, the region of interest (ROI) of the conductive arm point cloud with the best tightening boundary effect can be obtained from the point cloud of the target isolating switch.

[0009] Step 3: Employ a normal-based edge extraction algorithm to accurately extract the point cloud of the feature contour of the disconnector switch's conductive arm;

[0010] Step 4: Use the Euclidean clustering segmentation algorithm to cut the feature contour of the conductive arm and extract clusters that only contain the upper edge of the conductive arm;

[0011] Step 5: Using the RANSAC-based spatial line fitting algorithm, fit the left and right arms of the upper edge of the disconnector switch conductive arm into a spatial straight line, and calculate the included angle between the two arms. Define the fitting angle of the conductive arm based on the point cloud data as the closing angle θ. m The actual closing angle of the disconnector's conductive arm is the closing position angle θ. r Introducing the error factor α, the calculation formula is as follows:

[0012]

[0013] When α < 0.5%, it indicates that the disconnecting switch has been closed.

[0014] Preferably, in step 1, a KD-tree-based search algorithm is used to set linear search regions for the x, y, and z axes respectively. The x-axis search range is set to [0, 4.5], the y-axis search range is set to [-1, 2], and the z-axis search range is set to [-5, -2]. The three axes are merged to form a three-dimensional search region, thereby realizing the extraction of the region of interest (ROI) point cloud of the target disconnect switch.

[0015] Preferably, in step 2, an image target cropping algorithm is used to introduce a shrinkage factor τ in the x, y, and z axes, respectively. x τ y τ z For the contour shape of the conductive arm of the disconnector switch, τ x The value is set within the range of [4.13, 4.15], τ y The value is set within the range of [0.5, 0.6], τ z The value is set in the range [2.5, 4].

[0016] This invention also provides a method for determining whether a monitoring system is in normal operation based on the monitoring error rate of a disconnector switch closing status monitoring system, the method comprising:

[0017] The host computer's terminal management platform (8) can calculate the pulse width W of the smoke interference signal based on the smoke thickness z under the working conditions. r The calculation formula is:

[0018]

[0019] Based on the pulse width W of the smoke interference signal r The monitoring error rate of the system is calculated, and the system's operational status is determined based on this error rate. The formula for calculating the monitoring error rate γ of the disconnector switch closing status monitoring system is as follows:

[0020]

[0021] In the formula, R is the point cloud spacing setting value used to characterize the point cloud density; Ɛ is the point cloud density influence coefficient, taken as 175.81; W n The pulse width of the target isolating switch echo signal is set to 35ns.

[0022] The monitoring error rate γ obtained from the above formula, when γ>5%, indicates that the smoke has a large interference with the monitoring system of the disconnect switch closing status, affecting the normal operation of the monitoring system, and the terminal management platform (8) will issue an alarm signal.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] This invention enables real-time visual monitoring of disconnect switches by using lidar to capture images; based on point cloud image data processing technology for disconnect switches, it achieves accurate judgment of the disconnect switch's closing status, making it widely applicable; the monitoring system is more adaptable to dense smoke environments, has strong anti-interference capabilities, and high overall system stability. Attached Figure Description

[0025] The invention will now be further described with reference to the accompanying drawings, in which:

[0026] Figure 1 This is a schematic diagram of a disconnector switch closing status monitoring system suitable for environments with dense smoke. Detailed Implementation

[0027] The present invention will be further described below with reference to the accompanying drawings and embodiments, which are some of the embodiments of the present invention.

[0028] like Figure 1 As shown, this invention provides a system for monitoring the closing status of disconnecting switches in dense smoke environments. The system employs a lidar-based disconnecting switch closing status monitoring device, which includes a lidar (1), a disconnecting switch (2), a smoke concentration sensor (3), a slide rail (4), a network cable (5), and a host computer (6) equipped with a point cloud image data processing module (7) and a terminal management platform (8). The lidar (1) is fixed to the ground via the slide rail (4) and is used to acquire a three-dimensional image of the disconnecting switch (2) when it is closed and save it in the form of point cloud data. The network cable (5) transmits the point cloud data to the point cloud image data processing module (7) of the host computer; the smoke concentration sensor (3) monitors the smoke thickness value z under the working condition in real time and transmits the value to the terminal management platform (8) of the host computer through the WIFI module; the point cloud image data processing module (7) of the host computer performs visualization, region of interest (ROI) extraction and other processing on the collected point cloud data to complete the extraction of the closing status features of the disconnect switch; the terminal management platform (8) of the host computer displays the closing status of the disconnect switch and the monitoring error rate of the system on the interface.

[0029] This invention provides a method for processing point cloud image data of disconnect switches. The method includes the following steps:

[0030] Step 1: Use a KD-tree-based search algorithm to obtain the point cloud within the Region of Interest (ROI) of the target isolating switch from the input point cloud data;

[0031] Step 2: Using an image target cropping algorithm, a shrinkage factor τ is introduced in the x, y, and z axes respectively.x τ y τ z By changing the shrinkage factor value, the region of interest (ROI) of the conductive arm point cloud with the best tightening boundary effect can be obtained from the point cloud of the target isolating switch.

[0032] Step 3: Employ a normal-based edge extraction algorithm to accurately extract the point cloud of the feature contour of the disconnector switch's conductive arm;

[0033] Step 4: Use the Euclidean clustering segmentation algorithm to cut the feature contour of the conductive arm and extract clusters that only contain the upper edge of the conductive arm;

[0034] Step 5: Using the RANSAC-based spatial line fitting algorithm, fit the left and right arms of the upper edge of the disconnector switch conductive arm into a spatial straight line, and calculate the included angle between the two arms. Define the fitting angle of the conductive arm based on the point cloud data as the closing angle θ. m The actual closing angle of the disconnector's conductive arm is the closing position angle θ. r Introducing the error factor α, the calculation formula is as follows:

[0035]

[0036] When α < 0.5%, it indicates that the disconnecting switch has been closed.

[0037] Preferably, in step 1, a KD-tree-based search algorithm is used to set linear search regions for the x, y, and z axes respectively. The x-axis search range is set to [0, 4.5], the y-axis search range is set to [-1, 2], and the z-axis search range is set to [-5, -2]. The three axes are merged to form a three-dimensional search region, thereby realizing the extraction of the region of interest (ROI) point cloud of the target disconnect switch.

[0038] Preferably, in step 2, an image target cropping algorithm is used to introduce a shrinkage factor τ in the x, y, and z axes, respectively. x τ y τ z Regarding the contour shape of the conductive arm of the disconnector switch, in this embodiment τ x The value is set to 4.14, τ y The value is set to 0.53, τ z The value is set to 3.2.

[0039] This invention also provides a method for determining whether a monitoring system is in normal operation based on the monitoring error rate of a disconnector switch closing status monitoring system, the method comprising:

[0040] The host computer's terminal management platform (8) can calculate the pulse width W of the smoke interference signal based on the smoke thickness z under the working conditions. r The calculation formula is:

[0041]

[0042] Based on the pulse width W of the smoke interference signal r The monitoring error rate of the system is calculated, and the system's operational status is determined based on this error rate. The formula for calculating the monitoring error rate γ of the disconnector switch closing status monitoring system is as follows:

[0043]

[0044] In the formula, R is the point cloud spacing setting value used to characterize the point cloud density; Ɛ is the point cloud density influence coefficient, taken as 175.81; W n The pulse width of the target isolating switch echo signal is set to 35ns.

[0045] The monitoring error rate γ obtained from the above formula, when γ>5%, indicates that the smoke has a large interference with the monitoring system of the disconnect switch closing status, affecting the normal operation of the monitoring system, and the terminal management platform (8) will issue an alarm signal.

[0046] This invention is not limited to the specific embodiments described above. Under the guidance of this invention, without departing from the spirit and scope of the claims, appropriate modifications can be made to these features and embodiments to adapt to specific working scenarios, and all such modifications fall within the protection scope of this invention.

Claims

1. A system for monitoring the closing status of a disconnecting switch in a dense smoke environment. This system employs a lidar-based disconnecting switch closing status monitoring device, comprising a lidar, a disconnecting switch, a smoke concentration sensor, a slide rail, a network cable, and a host computer equipped with a point cloud image data processing module and a terminal management platform. The lidar is fixed to the ground via the slide rail and is used to acquire a three-dimensional image of the disconnecting switch when it is closed, saving it as point cloud data. The point cloud data is transmitted to the point cloud image data processing module of the host computer via the network cable. The smoke concentration sensor monitors the smoke thickness value z under the operating conditions in real time and transmits the value to the terminal management platform of the host computer via a WIFI module. The point cloud image data processing module of the host computer sequentially performs visualization, region of interest (ROI) extraction, edge extraction, upper edge extraction of the conductive arm, and spatial line fitting on the collected point cloud data to extract the closing status features of the disconnecting switch. The terminal management platform of the host computer displays the closing status of the disconnecting switch and the system's monitoring error rate on the interface. The point cloud image data processing module includes the following steps: Step 1: Use a KD-tree-based search algorithm to obtain the point cloud within the Region of Interest (ROI) of the target isolating switch from the input point cloud data; Step 2: Using an image target cropping algorithm, a shrinkage factor τ is introduced in the x, y, and z axes respectively. x τ y τ z By changing the shrinkage factor value, the region of interest (ROI) of the conductive arm point cloud with the best tightening boundary effect can be obtained from the point cloud of the target isolating switch. Step 3: Use a normal-based edge extraction algorithm to accurately extract the point cloud of the feature contour of the disconnector switch conductive arm; Step 4: Use the Euclidean clustering segmentation algorithm to cut the feature contour of the conductive arm and extract clusters that only contain the upper edge of the conductive arm; Step 5: Using the RANSAC-based spatial line fitting algorithm, fit the left and right arms of the upper edge of the disconnector switch conductive arm into a spatial straight line, and calculate the included angle between the two arms. Define the fitting angle of the conductive arm based on the point cloud data as the closing angle θ. m The actual closing angle of the disconnector's conductive arm is the closing position angle θ. r Introducing the error factor α, the calculation formula is as follows: When α < 0.5%, it indicates that the disconnecting switch has been closed.

2. The disconnector switch closing status monitoring system suitable for dense smoke environments according to claim 1, characterized in that, The host computer's terminal management platform can calculate the pulse width W of the smoke interference signal based on the smoke thickness z under the operating conditions. r The calculation formula is: Based on the pulse width W of the smoke interference signal r The monitoring error rate of the system is calculated, and the system's operational status is determined based on this error rate. The formula for calculating the monitoring error rate γ of the disconnector switch closing status monitoring system is as follows: In the formula, R is the point cloud spacing setting value used to characterize the point cloud density; Ɛ is the point cloud density influence coefficient, which is taken as 175.81; W n The pulse width of the target isolating switch echo signal is set to 35ns. The monitoring error rate γ obtained from the above formula indicates that when γ > 5%, the smoke has a significant impact on the monitoring system for the closing status of the disconnecting switch, affecting the normal operation of the monitoring system. The terminal management platform will then issue an alarm signal.

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