Airborne partial discharge detection device based on multi-sensor fusion and atlas detection

Abstract

The invention relates to an airborne partial discharge detection device based on multi-sensor fusion and atlas detection, which comprises an unmanned aerial vehicle platform and airborne partial discharge detection platforms, and is characterized in that the unmanned aerial vehicle platform is adaptively connected with the plurality of airborne partial discharge detection platforms through a double-platform mounting rack, and the airborne partial discharge detection platforms are connected with the double-platform mounting rack through a double-platform bracket; the unmanned aerial vehicle platform comprises a coaxial multi-rotor unmanned aerial vehicle; the airborne partial discharge detection platform comprises a multi-sensor fusion module, a multi-sensor monitoring module and a signal receiving and analyzing terminal; the airborne partial discharge detection platform establishes communication connection with the unmanned aerial vehicle platform in a WIFI wireless communication mode; the system has the advantages of reasonable structure, effective information acquisition, accurate positioning and rapid inspection.

Description

Technical Field

[0001] The present invention belongs to the technical field of substation inspection, and in particular relates to an airborne partial discharge detection device based on multi-sensor fusion and spectrum detection. Background Art

[0002] In order to address the pain points and difficulties of manual inspection, three types of intelligent devices, namely high-definition video, robots and drones, are usually configured in substations for data collection, and a substation intelligent inspection system is built to improve the intelligence and digitization level of equipment inspection. However, the traditional substation inspection method is that the operation and maintenance personnel drive to the target location and then use a telescope or infrared thermometer to conduct inspections on the inspection channel below the substation equipment. The inspection is time-consuming and labor-intensive, with a low level of intelligence and digitization, a long inspection time and low efficiency. In addition, there are often "dead corners" and blind spots above the equipment area, and manual inspections also have problems such as slow response to abnormal faults. Therefore, it is very necessary to provide an airborne partial discharge detection device based on multi-sensor fusion and spectrum detection with a reasonable structure, effective information collection, accurate positioning, and rapid inspection. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide an airborne partial discharge detection device based on multi-sensor fusion and spectrum detection, which has a reasonable structure, effectively collects information, performs accurate positioning, and realizes rapid inspection.

[0004] The object of the present invention is achieved in this way: an airborne partial discharge detection device based on multi-sensor fusion and spectrum detection comprises an unmanned aerial vehicle platform and an airborne partial discharge detection platform, the unmanned aerial vehicle platform is adaptively connected to a plurality of airborne partial discharge detection platforms through a double-stage mounting frame, and the airborne partial discharge detection platform is connected to a double-stage mounting frame through a double-stage bracket; the unmanned aerial vehicle platform comprises a coaxial multi-rotor unmanned aerial vehicle; the airborne partial discharge detection platform comprises a multi-sensor fusion module, a multi-sensor monitoring module and a signal receiving and analyzing terminal; the airborne partial discharge detection platform establishes a communication connection with the unmanned aerial vehicle platform through a WIFI wireless communication method.

[0005] The coaxial multi-rotor UAV comprises an RTK UAV positioning module, an airborne partial discharge fault monitoring module and a signal source device.

[0006] The multi-sensor fusion module of the airborne partial discharge detection platform includes a high-sensitivity partial discharge sensor, an ultrasonic sensor and a partial discharge collector; the high-sensitivity partial discharge sensor adopts an ultra-high frequency partial discharge sensor; the high-sensitivity partial discharge sensor is connected to the partial discharge collector.

[0007] The multi-sensor monitoring module of the airborne partial discharge detection platform includes a thermal imaging temperature measurement module, a video imaging module, a spectrum detection module, a visible light module and an infrared night vision module.

[0008] The signal receiving and analyzing terminal respectively establishes communication connections with the partial discharge collector, the ultra-high frequency partial discharge sensor, the video imaging module, the spectrum detection module and the airborne partial discharge fault detection module through wireless WIFI.

[0009] The partial discharge collector adopts an embedded system and has a signal sampling function. The sampling channels are divided into low-speed sampling channels and high-speed sampling channels. The former can collect multiple ultrasonic signals including AA ultrasound and AE ultrasound; the latter can collect multiple signals including high-frequency current signals and TEV signals. In terms of external design, the partial discharge collector adopts a liquid crystal screen, and its instrument switch has an automatic switching function. The network signal is amplified by filtering through digital-to-analog conversion and then sent to the earphone. In terms of information storage, the partial discharge collector is equipped with multiple memories and USB interfaces, and the communication method is expanded using WIFI.

[0010] The UHF partial discharge sensor and the spectrum detection module cooperate to adopt the partial discharge signal monitoring and positioning method based on the UHF sensor spectrum detection to realize the monitoring of partial discharge signals and spatial electromagnetic interference signals. Combined with the spatial orientation of the UHF sensor, the spatial electromagnetic interference is eliminated and the defect position is located through the UHF signal strength and feature similarity. Specifically: ① Encode the sensor, associate the coding information with the spatial position of the high-voltage equipment, and obtain the sensor space topology map; ② Perform abnormal judgment on the UHF signal collected by each sensor, and form an abnormal sensor set with sensors with abnormal data; ③ Perform similarity calculation on the signals of each sensor, and divide the abnormal sensor set into multiple abnormal sensor groups; ④ Perform internal defect identification on each abnormal sensor group to eliminate interference signals; ⑤ Position the internal defects of the abnormal sensor group.

[0011] The partial discharge signal monitoring and positioning method based on UHF sensor spectrum detection specifically includes the following steps:

[0012] Step 1: Sensor spatial position association: Encoding the sensors deployed on high-voltage equipment is the basic step to establish the spatial association between high-voltage equipment and sensors. The encoding rule is site name_device name_installation location_sensor number. The site name is the name of the substation where the high-voltage equipment is located, and the device name is the name of the high-voltage equipment. The site name and device name need to be consistent with the substation ledger information; the installation location is the information where the sensor is located; according to the spatial structure of the high-voltage equipment, the power connection diagram and the sensor installation location, the coded sensor is associated with the spatial location of the high-voltage equipment to establish a sensor space topology map. The topology map includes not only the spatial location information of the sensor and the high-voltage equipment, but also the electrical connection information of each sensor on the propagation path of the partial discharge signal;

[0013] Step 2: UHF signal abnormality judgment: When partial discharge or external interference occurs, the spectrum data collected by the UHF sensor will show partial discharge characteristics, and the electromagnetic signal strength will be greater than normal. Since the external interference signal will increase the electromagnetic signal strength, it is impossible to eliminate the influence of interference by comparing the signal strength threshold to judge the signal abnormality. Therefore, based on the spectrum detection module, a method combining spectrum recognition based on deep learning and UHF signal strength comparison is used to judge whether the signal is abnormal, eliminate the influence of some interference, and improve the accuracy of signal abnormality judgment;

[0014] Step 3: Abnormal sensor grouping: According to the attenuation characteristics of electromagnetic waves, the electromagnetic wave signal strength emitted by the same discharge source will attenuate to varying degrees along the propagation path, but the characteristics of partial discharge will not change. The similarity of signal characteristics can be used to determine the homology of multiple abnormal sensors, group the abnormal sensors, and identify the number of partial discharge sources;

[0015] Step 4: Eliminate spatial interference: The discharge source of each abnormal sensor group may be the local discharge signal inside the high-voltage equipment, or it may be the spatial interference signal outside the high-voltage equipment. According to the characteristic that electromagnetic signals attenuate with increasing distance, the spatial interference signal can be eliminated by comparing the signal strength of the UHF data of each sensor in a single abnormal sensor group;

[0016] Step 5: Spatial positioning: Based on the characteristic that the signal generated by the same discharge source has different attenuation degrees when reaching sensors at different positions, for the same internal partial discharge source, the approximate position of the partial discharge source is determined by comparing the signal strength collected by sensors at different positions, and the internal partial discharge defect is located.

[0017] The abnormal judgment of the UHF signal in step 2 specifically includes the following steps:

[0018] Step 2.1: Use the deep learning-based spectrum recognition method to diagnose the spectrum data collected by the UHF sensor for the i-th time, and preliminarily identify the sensor with abnormal data;

[0019] Step 2.2: Calculate the number of pulses N of the i-th data collected by the UHF sensor with abnormal data i and the average discharge amplitude F i , the UHF signal strength is: V i =N i ×F i (1);

[0020] Step 2.3: Calculate the cumulative average signal strength of the data collected before the i-th time by the UHF sensor with abnormal data: Where t is the length of the cumulative time window, indicating the number of cumulative times selected; N jF is the number of pulses of the UHF sensor collecting normal data for the jth time; j is the average discharge amplitude of the UHF sensor collecting normal data for the jth time;

[0021] Step 2.4: Filter out sensors with abnormal data. i >V j When the sensor is detected, the sensor is added to the abnormal sensor set.

[0022] The abnormal sensor grouping in step 3 specifically includes the following steps:

[0023] Step 3.1: Adjust the collection frequency of the sensors in the abnormal sensor set, trigger the collection of n groups of ultra-high frequency abnormal data at the same time, and perform denoising on the data;

[0024] Step 3.2: Calculate the spatial features of each set of UHF data, including 21 characteristic values ​​such as the number of pulses of the discharge signal, the concentration of the phase distribution of the discharge times, and the average amplitude;

[0025] Step 3.3: Perform Z-Score standardization on spatial features: In the formula, is the standardized value of the jth eigenvalue of the i-th group of data, 1≤i≤n, 1≤j≤21; E ij is the jth eigenvalue of the i-th group of data; E j is the mean of the jth eigenvalue of n sets of data; s j is the standard deviation of the ith eigenvalue of n sets of data;

[0026] Step 3.4: Calculate the correlation between the i-th group of data of any two sensors as follows: Where A and B are the codes of any two abnormal sensors; sim AB (i) is the spatial characteristic similarity of the i-th group of UHF data of two sensors A and B; is the normalized value of the jth eigenvalue of the i-th group of data of sensor A; is the normalized value of the jth eigenvalue of the i-th group of data of sensor B;

[0027] Step 3.5: Calculate the mean similarity of n groups of UHF anomaly data of sensors A and B:

[0028]

[0029] Step 3.6: Abnormal sensor grouping: When When the abnormal data monitored by sensors A and B are judged to be related and come from the same discharge source; the abnormal sensors are grouped using the threshold of similarity, and the sensors that monitor the ultra-high frequency data of the same discharge source are grouped into one group; where St is the similarity threshold.

[0030] Beneficial effects of the invention: The present invention is an airborne partial discharge detection device based on multi-sensor fusion and spectrum detection. In use, the present invention has the following advantages: ① It can carry multiple sensors and integrate multiple detection methods including partial discharge detection, infrared temperature measurement detection, and video imaging; ② It uses the unmanned aerial vehicle platform to adopt a non-contact detection method, which is light in weight and fully adapted to the unmanned aerial vehicle operating platform, with a high degree of integration, convenient operation, and safety and reliability; ③ The device has high detection sensitivity and can effectively diagnose faults such as internal discharge, suspended discharge, and tip discharge of substation equipment, and determine the fault location; ④ It is adaptable to complex interference environments, suppresses various external interferences through multiple anti-interference means, improves the signal-to-noise ratio, and effectively improves the accuracy and sensitivity of partial discharge monitoring and detection, which can solve the problems of large signal attenuation and blindness in traditional ground detection. ⑤ Thermal imaging temperature measurement detects the temperature and operating status of the equipment in operation, which can easily, safely, intuitively and accurately find and judge the overheating fault of the equipment; ⑥ It has the functions of amplitude detection, phase spectrum detection, pulse spectrum detection and waveform spectrum detection, and has PRPD and PRPS spectrum functions; ⑦ The device integrates local discharge signals and temperature signals, which can provide a strong basis for the refined inspection and maintenance of the equipment, eliminate equipment accidents in the bud in time, and effectively avoid accidents caused by equipment abnormalities; ⑧ The device obtains partial discharge data by wireless WIFI, quickly and accurately judges the location and morphology of the partial discharge fault point, and combines video imaging technology to take pictures of the defect points for evidence collection; The present invention has the advantages of reasonable structure, effective information collection, accurate positioning, and rapid inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a configuration schematic diagram of the present invention.

[0032] Figure 2 It is a structural block diagram of the present invention.

[0033] Figure 3 The overall structure diagram of the present invention is Figure 1 .

[0034] Figure 4 The overall structure diagram of the present invention is Figure 2 .

[0035] Figure 5 It is a schematic diagram of the installation structure of the airborne partial discharge detection platform of the present invention.

[0036] Figure 6 It is a structural schematic diagram of the partial discharge collector of the present invention.

[0037] Figure 7 It is a schematic diagram of the top-added monopole antenna of the present invention.

[0038] Figure 8 It is a schematic diagram of the positioning process of the present invention.

[0039] Fig. 9 This is a flow chart of eliminating spatial interference of the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further described below in conjunction with the accompanying drawings.

[0041] Example 1

[0042] like Figure 1-9 As shown, an airborne partial discharge detection device based on multi-sensor fusion and spectrum detection includes an unmanned aerial vehicle platform and an airborne partial discharge detection platform. The unmanned aerial vehicle platform is adaptably connected to multiple airborne partial discharge detection platforms through a double-stage mounting frame, and the airborne partial discharge detection platform is connected to a double-stage mounting frame through a double-stage bracket; the unmanned aerial vehicle platform includes a coaxial multi-rotor unmanned aerial vehicle; the airborne partial discharge detection platform includes a multi-sensor fusion module, a multi-sensor monitoring module and a signal receiving and analysis terminal; the airborne partial discharge detection platform establishes a communication connection with the unmanned aerial vehicle platform through WIFI wireless communication.

[0043] The coaxial multi-rotor UAV comprises an RTK UAV positioning module, an airborne partial discharge fault monitoring module and a signal source device.

[0044] The multi-sensor fusion module of the airborne partial discharge detection platform includes a high-sensitivity partial discharge sensor, an ultrasonic sensor and a partial discharge collector; the high-sensitivity partial discharge sensor adopts an ultra-high frequency partial discharge sensor; the high-sensitivity partial discharge sensor is connected to the partial discharge collector.

[0045] In this embodiment, the UHF partial discharge sensor adopts a Goubau antenna, which has the advantages of a wide frequency band, a good directional pattern, and a relatively small size. The Goubau antenna is developed from a topped monopole antenna. The schematic diagram of the topped monopole antenna is shown in FIG. Figure 7 As shown, r is the radius of the top disk, h is the height of the disk from the ground (or grounding electrode); where C a is the internal capacitance, C e is the external stray capacitance, R r is the radiation resistance, and the three are defined as follows: C a =ε0πr 2 / h, The top of the Goubau antenna is composed of four disc-shaped conductors, which are divided into two pairs with larger curvature (hereinafter referred to as A pair of disc-shaped conductors) and another pair with smaller curvature (hereinafter referred to as B pair of disc-shaped conductors). Each disc-shaped conductor is connected to a cylindrical conductor at the bottom. The two cylindrical conductors of the A pair of disc-shaped conductors have the same diameter, and the two cylindrical conductors of the B pair of disc-shaped conductors have the same diameter. However, the diameters of the two pairs of cylindrical conductors are not necessarily the same and can be adjusted according to actual conditions to achieve a certain effect. The A pair of disc-shaped conductors serve as the antenna receiving pole, and the lower ends of the two cylindrical conductors are connected by a flat copper bar, and the center of the flat copper bar is connected to an N-type adapter with an impedance of 50Ω. The B pair of disc-shaped conductors serve as the grounding pole, which is directly connected to the ground. Under the disc-shaped conductors, there are coils connecting the four cylindrical conductors. These conductor coils have two functions: forming reactance at high frequencies to balance the overall reactance of the antenna in order to obtain a suitable impedance; short-circuiting the antenna receiving pole and the grounding pole at low frequencies to filter out low-frequency interference.

[0046] The multi-sensor monitoring module of the airborne partial discharge detection platform includes a thermal imaging temperature measurement module, a video imaging module, a spectrum detection module, a visible light module and an infrared night vision module.

[0047] The signal receiving and analyzing terminal respectively establishes communication connections with the partial discharge collector, the ultra-high frequency partial discharge sensor, the video imaging module, the spectrum detection module and the airborne partial discharge fault detection module through wireless WIFI.

[0048] The partial discharge collector adopts an embedded system and has a signal sampling function. The sampling channels are divided into low-speed sampling channels and high-speed sampling channels. The former can collect multiple ultrasonic signals including AA ultrasound and AE ultrasound; the latter can collect multiple signals including high-frequency current signals and TEV signals. In terms of external design, the partial discharge collector adopts a liquid crystal screen, and its instrument switch has an automatic switching function. The network signal is amplified by filtering through digital-to-analog conversion and then sent to the earphone. In terms of information storage, the partial discharge collector is equipped with multiple memories and USB interfaces, and the communication method is expanded using WIFI.

[0049] In this embodiment, the present invention uses a drone platform as a carrier, and comprehensively utilizes advanced information technology to assist in effective information collection, accurate positioning, and solve related problems; and configures a dual-pan-tilt mounting frame to realize the function of mounting multiple sensors on one drone platform; selects a standard dual-pan-tilt bracket to meet the functions of installation balance, interface adaptation, etc.; the present invention adopts a non-contact detection method to achieve rapid inspection of partial discharge and heating defects of open high-voltage equipment; the device is adapted to complex interference environments, and adopts a variety of anti-interference means to suppress various external interferences, improve the signal-to-noise ratio, and accurately detect the specific discharge position and discharge intensity of high-voltage equipment through a high-sensitivity partial discharge sensor, and timely discover the internal discharge of high-voltage bushing products; at the same time, the thermal imaging temperature measurement module can simply, safely, intuitively, and accurately find and judge equipment overheating faults; the signal receiving and analysis terminal obtains partial discharge data in a wireless WIFI manner, quickly and accurately judges the location and shape of the partial discharge fault point, and combines video imaging technology to take pictures of the defect points for evidence collection;

[0050] In this embodiment, the method of using the device of the present invention is as follows: General startup process: ① Device startup: First, long press the switch to turn on the device and wait for connection. After the device is turned on, the WIFI hotspot and Bluetooth will start. Other devices connected to Bluetooth and hotspots can obtain collected data and camera images; ② Software startup: After the software is started, the initialization work will be performed, and the corresponding permission application will be made on the opening interface. If the permission application is not passed, the software will automatically exit; When the software is started, a new directory / storage / emulated / 0 / -300 will be automatically created; ③ Bluetooth connection: After the software is started, it will automatically make an initial trial connection. The software will automatically connect according to the last Bluetooth connection situation. If the device has been started at this time, data collection, processing and display will begin; if the device has not been started or has not been connected, you can call out the menu bar in the upper right corner and jump to the Bluetooth connection interface by clicking the Bluetooth menu to connect; ④ WIFI connection: After the software is started, a WIFI network scan will be performed at the same time. If the device has been started and the tablet has been connected to the device WIFI, it will automatically start to acquire video images; if the device is not started or the device WIFI is not connected, it will prompt that the connection failed. The user needs to adjust the WIFI connection and manually click the play button to connect again; ⑤ Data configuration: After the software is started for the first time, a configuration file named config.properties will be generated in / storage / emulated / 0 / -300. The configuration file has default information. The configuration file will be loaded each time the software is started. The user can control the software data display by modifying the configuration file information; ⑥ Save: After clicking, the test data at this time will be saved to an xml file, which can be read or report generated; ⑦ Report generation: Click Generate report, and the following report generation interface will pop up. You can select all the saved xml data files in this interface to generate a word report. After clicking Generate report, the software will automatically extract the selected xml file data for integration and generate a word document, which will then jump to open for reading.

[0051] The present invention is an airborne partial discharge detection device based on multi-sensor fusion and spectrum detection. In use, the present invention has the following advantages: ① It can carry multiple sensors and integrate multiple detection methods including partial discharge detection, infrared temperature measurement detection, and video imaging; ② It uses a non-contact detection method using an unmanned aerial vehicle platform, is light in weight, and is fully adapted to the unmanned aerial vehicle operating platform, with a high degree of integration, convenient operation, and safety and reliability; ③ The device has high detection sensitivity and can effectively diagnose faults such as internal discharge, suspended discharge, and tip discharge in substation equipment, and determine the fault location; ④ It is adaptable to complex interference environments, suppresses various external interferences through multiple anti-interference means, improves the signal-to-noise ratio, and effectively improves the accuracy and sensitivity of partial discharge monitoring and detection, which can solve the problems of large signal attenuation and blind spots in traditional ground detection. ; ⑤ Thermal imaging temperature measurement detects the temperature and operating status of the equipment in operation, which can simply, safely, intuitively and accurately find and judge the overheating fault of the equipment; ⑥ It has the functions of amplitude detection, phase spectrum detection, pulse spectrum detection and waveform spectrum detection, and has PRPD and PRPS spectrum functions; ⑦ The device integrates local discharge signals and temperature signals, which can provide a strong basis for the refined inspection and maintenance of the equipment, eliminate equipment accidents in the bud in time, and effectively avoid accidents caused by equipment abnormalities; ⑧ The device obtains partial discharge data by wireless WIFI, quickly and accurately judges the location and morphology of the partial discharge fault point, and combines video imaging technology to take pictures of the defect points for evidence collection; The present invention has the advantages of reasonable structure, effective information collection, accurate positioning, and rapid inspection.

[0052] Example 2

[0053] like Figure 1-9 As shown, an airborne partial discharge detection device based on multi-sensor fusion and spectrum detection includes an unmanned aerial vehicle platform and an airborne partial discharge detection platform. The unmanned aerial vehicle platform is adaptably connected to multiple airborne partial discharge detection platforms through a double-stage mounting frame, and the airborne partial discharge detection platform is connected to a double-stage mounting frame through a double-stage bracket; the unmanned aerial vehicle platform includes a coaxial multi-rotor unmanned aerial vehicle; the airborne partial discharge detection platform includes a multi-sensor fusion module, a multi-sensor monitoring module and a signal receiving and analysis terminal; the airborne partial discharge detection platform establishes a communication connection with the unmanned aerial vehicle platform through WIFI wireless communication.

[0054] The UHF partial discharge sensor and the spectrum detection module cooperate to adopt the partial discharge signal monitoring and positioning method based on the UHF sensor spectrum detection to realize the monitoring of partial discharge signals and spatial electromagnetic interference signals. Combined with the spatial orientation of the UHF sensor, the spatial electromagnetic interference is eliminated and the defect position is located through the UHF signal strength and feature similarity. Specifically: ① Encode the sensor, associate the coding information with the spatial position of the high-voltage equipment, and obtain the sensor space topology map; ② Perform abnormal judgment on the UHF signal collected by each sensor, and form an abnormal sensor set with sensors with abnormal data; ③ Perform similarity calculation on the signals of each sensor, and divide the abnormal sensor set into multiple abnormal sensor groups; ④ Perform internal defect identification on each abnormal sensor group to eliminate interference signals; ⑤ Position the internal defects of the abnormal sensor group.

[0055] The partial discharge signal monitoring and positioning method based on UHF sensor spectrum detection specifically includes the following steps:

[0056] Step 1: Sensor spatial position association: Encoding the sensors deployed on high-voltage equipment is the basic step to establish the spatial association between high-voltage equipment and sensors. The encoding rule is site name_device name_installation location_sensor number. The site name is the name of the substation where the high-voltage equipment is located, and the device name is the name of the high-voltage equipment. The site name and device name need to be consistent with the substation ledger information; the installation location is the information where the sensor is located; according to the spatial structure of the high-voltage equipment, the power connection diagram and the sensor installation location, the coded sensor is associated with the spatial location of the high-voltage equipment to establish a sensor space topology map. The topology map includes not only the spatial location information of the sensor and the high-voltage equipment, but also the electrical connection information of each sensor on the propagation path of the partial discharge signal;

[0057] Step 2: UHF signal abnormality judgment: When partial discharge or external interference occurs, the spectrum data collected by the UHF sensor will show partial discharge characteristics, and the electromagnetic signal strength will be greater than normal. Since the external interference signal will increase the electromagnetic signal strength, it is impossible to eliminate the influence of interference by comparing the signal strength threshold to judge the signal abnormality. Therefore, based on the spectrum detection module, a method combining spectrum recognition based on deep learning and UHF signal strength comparison is used to judge whether the signal is abnormal, eliminate the influence of some interference, and improve the accuracy of signal abnormality judgment;

[0058] Step 3: Abnormal sensor grouping: According to the attenuation characteristics of electromagnetic waves, the electromagnetic wave signal strength emitted by the same discharge source will attenuate to varying degrees along the propagation path, but the characteristics of partial discharge will not change. The similarity of signal characteristics can be used to determine the homology of multiple abnormal sensors, group the abnormal sensors, and identify the number of partial discharge sources;

[0059] Step 4: Eliminate spatial interference: The discharge source of each abnormal sensor group may be the local discharge signal inside the high-voltage equipment, or it may be the spatial interference signal outside the high-voltage equipment. According to the characteristic that the electromagnetic signal attenuates with increasing distance, the signal strength of the UHF data of each sensor in a single abnormal sensor group can be compared to eliminate the spatial interference signal. The specific process is as follows: Fig. 9 As shown;

[0060] In this embodiment, eliminating spatial interference specifically includes the following steps:

[0061] Step 4.1: Check whether the abnormal sensor group contains a space UHF partial discharge sensor. If not, determine that the discharge source of the abnormal sensor group is internal partial discharge, otherwise proceed to the next step;

[0062] Step 4.2: Filter the detection data of the space UHF partial discharge sensor to remove noise interference;

[0063] Step 4.3: Calculate the mean signal strength of each UHF sensor in the abnormal sensor group. The formula for calculating the mean signal strength is: Where V Av is the mean signal strength of sensor A; N Ai is the number of pulses of the i-th group signal of sensor A; F Ai is the average discharge amplitude of the i-th group signal of sensor A; n is the number of abnormal data groups collected by the abnormal sensor group;

[0064] Step 4.4: Compare the mean values ​​of the signal strengths of the sensors. If the maximum value is that of the spatial UHF PD sensor, it is determined that the discharge source of the abnormal sensors is an external interference signal. If the maximum value is that of the contact UHF PD sensor, it is determined that the discharge source of the abnormal sensors is an internal PD signal.

[0065] Step 5: Spatial positioning: Based on the characteristic that the signal generated by the same discharge source has different attenuation degrees when reaching sensors at different positions, for the same internal partial discharge source, the approximate position of the partial discharge source is determined by comparing the signal strength collected by sensors at different positions, and the internal partial discharge defect is located.

[0066] In this embodiment, spatial positioning specifically includes the following steps:

[0067] Step 5.1: According to formula (6), the PD UHF sensors A and B with the largest and second largest signal strength mean values ​​are obtained. Combined with the sensor spatial topology, the high-voltage equipment that is directly electrically connected to sensors A and B is considered suspected discharge. At the same time, the equipment that only has one sensor A installed is listed as suspected discharge.

[0068] Step 5.2: Use the median perpendicular line between sensor A and its adjacent sensors to segment the suspected discharge, and obtain the area between the median perpendicular line and sensor A as the suspected discharge area; if only one sensor is deployed in the suspected discharge air chamber, the air chamber is the suspected discharge area without segmentation;

[0069] Step 5.3: Determine that the intersection of the n suspected discharge regions is the region where the partial discharge source is located; if the n suspected discharge regions have no intersection, determine that the union of the n suspected discharge regions is the region where the partial discharge source is located.

[0070] The abnormal judgment of the UHF signal in step 2 specifically includes the following steps:

[0071] Step 2.1: Use the deep learning-based spectrum recognition method to diagnose the spectrum data collected by the UHF sensor for the i-th time, and preliminarily identify the sensor with abnormal data;

[0072] Step 2.2: Calculate the number of pulses N of the i-th data collected by the UHF sensor with abnormal data i and the average discharge amplitude F i , the UHF signal strength is: V i =N i ×F i (1);

[0073] Step 2.3: Calculate the cumulative average signal strength of the data collected before the i-th time by the UHF sensor with abnormal data: Where t is the length of the cumulative time window, indicating the number of cumulative times selected; N j F is the number of pulses of the UHF sensor collecting normal data for the jth time; j is the average discharge amplitude of the UHF sensor collecting normal data for the jth time;

[0074] Step 2.4: Filter out sensors with abnormal data. i >V j When a sensor is detected, it is added to the abnormal sensor set; the abnormal sensor set consists of multiple sensors that have collected abnormal data. The cause of the data anomaly may be partial discharge inside the high-voltage equipment or external discharge interference, where the discharge source may be one or more. To distinguish and locate the discharge source, the abnormal sensor set must first be grouped.

[0075] The abnormal sensor grouping in step 3 specifically includes the following steps:

[0076] Step 3.1: Adjust the collection frequency of the sensors in the abnormal sensor set, trigger the collection of n groups of ultra-high frequency abnormal data at the same time, and perform denoising on the data;

[0077] Step 3.2: Calculate the spatial features of each set of UHF data, including 21 characteristic values ​​such as the number of pulses of the discharge signal, the concentration of the phase distribution of the discharge times, and the average amplitude;

[0078] Step 3.3: Perform Z-Score standardization on spatial features: In the formula, is the standardized value of the jth eigenvalue of the i-th group of data, 1≤i≤n, 1≤j≤21; E ij is the jth eigenvalue of the i-th group of data; E j is the mean of the jth eigenvalue of n sets of data; s j is the standard deviation of the ith eigenvalue of n sets of data;

[0079] Step 3.4: Calculate the correlation between the i-th group of data of any two sensors as follows: Where A and B are the codes of any two abnormal sensors; sim AB (i) is the spatial characteristic similarity of the i-th group of UHF data of two sensors A and B; is the normalized value of the jth eigenvalue of the i-th group of data of sensor A; is the normalized value of the jth eigenvalue of the i-th group of data of sensor B;

[0080] Step 3.5: Calculate the mean similarity of n groups of UHF anomaly data of sensors A and B:

[0081]

[0082] Step 3.6: Abnormal sensor grouping: When When the abnormal data monitored by sensors A and B are judged to be related and come from the same discharge source; the abnormal sensors are grouped using the threshold of similarity, and the sensors that monitor the ultra-high frequency data of the same discharge source are grouped into one group; where S t is the similarity threshold, and by counting the similarities of 200 sets of data, we get S t When the value is 0.65, the correlation of the data can be better judged; by analyzing the similarity of signal features, the homology of the signals can be judged, different discharge sources can be effectively identified, and the accuracy of positioning can be improved.

[0083] The present invention is an airborne partial discharge detection device based on multi-sensor fusion and spectrum detection. In use, the present invention has the following advantages: ① It can carry multiple sensors and integrate multiple detection methods including partial discharge detection, infrared temperature measurement detection, and video imaging; ② It uses the unmanned aerial vehicle platform to adopt a non-contact detection method, which is light in weight and fully adapted to the unmanned aerial vehicle operating platform, with a high degree of integration, convenient operation, and safety and reliability; ③ The device has high detection sensitivity and can effectively diagnose faults such as internal discharge, suspended discharge, and tip discharge of substation equipment, and determine the fault location; ④ It is adaptable to complex interference environments, suppresses various external interferences through multiple anti-interference means, improves the signal-to-noise ratio, and effectively improves the accuracy and sensitivity of partial discharge monitoring and detection, which can solve the problems of large signal attenuation and high noise in traditional ground detection. There are problems such as blind spots; ⑤ Thermal imaging temperature measurement detects the temperature and operating status of the equipment in operation, which can simply, safely, intuitively and accurately find and judge the overheating fault of the equipment; ⑥ It has amplitude detection, phase spectrum detection, pulse spectrum detection and waveform spectrum detection functions, and has PRPD and PRPS spectrum functions; ⑦ The device integrates local discharge signals and temperature signals, which can provide a strong basis for the refined inspection and maintenance of the equipment, and effectively avoid accidents caused by equipment abnormalities; ⑧ The device obtains partial discharge data by wireless WIFI, quickly and accurately determines the location and morphology of the partial discharge fault point, and combines video imaging technology to take pictures of the defect points for evidence collection; The present invention has the advantages of reasonable structure, effective information collection, accurate positioning, and rapid inspection.

Claims

1. An airborne partial discharge detection device based on multi-sensor fusion and spectrum detection, which includes an unmanned aerial vehicle platform and an airborne partial discharge detection platform, and is characterized by: The UAV platform is adaptably connected to multiple airborne partial discharge detection platforms through a double-stage mounting frame, and the airborne partial discharge detection platform is connected to the double-stage mounting frame through a double-stage bracket; the UAV platform includes a coaxial multi-rotor UAV; the airborne partial discharge detection platform includes a multi-sensor fusion module, a multi-sensor monitoring module and a signal receiving and analysis terminal; the airborne partial discharge detection platform establishes a communication connection with the UAV platform through WIFI wireless communication.

2. The airborne partial discharge detection device based on multi-sensor fusion and spectrum detection according to claim 1, characterized in that: The coaxial multi-rotor UAV comprises an RTK UAV positioning module, an airborne partial discharge fault monitoring module and a signal source device.

3. The airborne partial discharge detection device based on multi-sensor fusion and spectrum detection according to claim 1, characterized in that: The multi-sensor fusion module of the airborne partial discharge detection platform includes a high-sensitivity partial discharge sensor, an ultrasonic sensor and a partial discharge collector; the high-sensitivity partial discharge sensor adopts an ultra-high frequency partial discharge sensor; the high-sensitivity partial discharge sensor is connected to the partial discharge collector.

4. The airborne partial discharge detection device based on multi-sensor fusion and spectrum detection as claimed in claim 3, characterized in that: The multi-sensor monitoring module of the airborne partial discharge detection platform includes a thermal imaging temperature measurement module, a video imaging module, a spectrum detection module, a visible light module and an infrared night vision module.

5. The airborne partial discharge detection device based on multi-sensor fusion and spectrum detection according to claim 1 or 4, characterized in that: The signal receiving and analyzing terminal respectively establishes communication connections with the partial discharge collector, the ultra-high frequency partial discharge sensor, the video imaging module, the spectrum detection module and the airborne partial discharge fault detection module through wireless WIFI.

6. The airborne partial discharge detection device based on multi-sensor fusion and spectrum detection as claimed in claim 3, characterized in that: The partial discharge collector adopts an embedded system and has a signal sampling function. The sampling channels are divided into low-speed sampling channels and high-speed sampling channels. The former can collect multiple ultrasonic signals including AA ultrasound and AE ultrasound; the latter can collect multiple signals including high-frequency current signals and TEV signals. In terms of external design, the partial discharge collector adopts a liquid crystal screen, and its instrument switch has an automatic switching function. The network signal is amplified by filtering through digital-to-analog conversion and then sent to the earphone. In terms of information storage, the partial discharge collector is equipped with multiple memories and USB interfaces, and the communication method is expanded using WIFI.

7. The airborne partial discharge detection device based on multi-sensor fusion and spectrum detection according to claim 5, characterized in that: The UHF partial discharge sensor and the spectrum detection module cooperate to adopt the partial discharge signal monitoring and positioning method based on the UHF sensor spectrum detection to realize the monitoring of partial discharge signals and spatial electromagnetic interference signals. Combined with the spatial orientation of the UHF sensor, the spatial electromagnetic interference is eliminated and the defect position is located through the UHF signal strength and feature similarity. Specifically: ① Encode the sensor, associate the coding information with the spatial position of the high-voltage equipment, and obtain the sensor space topology map; ② Perform abnormal judgment on the UHF signal collected by each sensor, and form an abnormal sensor set with sensors with abnormal data; ③ Perform similarity calculation on the signals of each sensor, and divide the abnormal sensor set into multiple abnormal sensor groups; ④ Perform internal defect identification on each abnormal sensor group to eliminate interference signals; ⑤ Position the internal defects of the abnormal sensor group.

8. The airborne partial discharge detection device based on multi-sensor fusion and spectrum detection according to claim 7, characterized in that: The partial discharge signal monitoring and positioning method based on UHF sensor spectrum detection specifically includes the following steps: Step 1: Sensor spatial position association: Encoding the sensors deployed on high-voltage equipment is the basic step to establish the spatial association between high-voltage equipment and sensors. The encoding rule is site name_device name_installation location_sensor number. The site name is the name of the substation where the high-voltage equipment is located, and the device name is the name of the high-voltage equipment. The site name and device name need to be consistent with the substation ledger information; the installation location is the information where the sensor is located; according to the spatial structure of the high-voltage equipment, the power connection diagram and the sensor installation location, the coded sensor is associated with the spatial location of the high-voltage equipment to establish a sensor space topology map. The topology map includes not only the spatial location information of the sensor and the high-voltage equipment, but also the electrical connection information of each sensor on the propagation path of the partial discharge signal; Step 2: UHF signal abnormality judgment: When partial discharge or external interference occurs, the spectrum data collected by the UHF sensor will show partial discharge characteristics, and the electromagnetic signal strength will be greater than normal. Since the external interference signal will increase the electromagnetic signal strength, it is impossible to eliminate the influence of interference by comparing the signal strength threshold to judge the signal abnormality. Therefore, based on the spectrum detection module, a method combining spectrum recognition based on deep learning and UHF signal strength comparison is used to judge whether the signal is abnormal, eliminate the influence of some interference, and improve the accuracy of signal abnormality judgment; Step 3: Abnormal sensor grouping: According to the attenuation characteristics of electromagnetic waves, the electromagnetic wave signal strength emitted by the same discharge source will attenuate to varying degrees along the propagation path, but the characteristics of partial discharge will not change. The similarity of signal characteristics can be used to determine the homology of multiple abnormal sensors, group the abnormal sensors, and identify the number of partial discharge sources; Step 4: Eliminate spatial interference: The discharge source of each abnormal sensor group may be the local discharge signal inside the high-voltage equipment, or it may be the spatial interference signal outside the high-voltage equipment. According to the characteristic that electromagnetic signals attenuate with increasing distance, the spatial interference signal can be eliminated by comparing the signal strength of the UHF data of each sensor in a single abnormal sensor group; Step 5: Spatial positioning: Based on the characteristic that the signal generated by the same discharge source has different attenuation degrees when reaching sensors at different positions, for the same internal partial discharge source, the approximate position of the partial discharge source is determined by comparing the signal strength collected by sensors at different positions, and the internal partial discharge defect is located.

9. The airborne partial discharge detection device based on multi-sensor fusion and spectrum detection according to claim 8, characterized in that: The abnormal judgment of the UHF signal in step 2 specifically includes the following steps: Step 2.1: Use the deep learning-based spectrum recognition method to diagnose the spectrum data collected by the UHF sensor for the i-th time, and preliminarily identify the sensor with abnormal data; Step 2.2: Calculate the number of pulses N of the i-th data collected by the UHF sensor with abnormal data i and the average discharge amplitude F i , the UHF signal strength is: V i =N i ×F i (1); Step 2.3: Calculate the cumulative average signal strength of the data collected before the i-th time by the UHF sensor with abnormal data: Where t is the length of the cumulative time window, indicating the number of cumulative times selected; N j F is the number of pulses of the UHF sensor collecting normal data for the jth time; j is the average discharge amplitude of the UHF sensor collecting normal data for the jth time; Step 2.4: Filter out sensors with abnormal data. i >V j When the sensor is detected, the sensor is added to the abnormal sensor set.

10. The airborne partial discharge detection device based on multi-sensor fusion and spectrum detection according to claim 8, characterized in that: The abnormal sensor grouping in step 3 specifically includes the following steps: Step 3.1: Adjust the collection frequency of the sensors in the abnormal sensor set, trigger the collection of n groups of ultra-high frequency abnormal data at the same time, and perform denoising on the data; Step 3.2: Calculate the spatial features of each set of UHF data, including 21 characteristic values ​​such as the number of pulses of the discharge signal, the concentration of the phase distribution of the discharge times, and the average amplitude; Step 3.3: Perform Z-Score standardization on spatial features: In the formula, is the standardized value of the jth eigenvalue of the i-th group of data, 1≤i≤n, 1≤j≤21; E ij is the jth eigenvalue of the i-th group of data; E j is the mean of the jth eigenvalue of n sets of data; s j is the standard deviation of the ith eigenvalue of n sets of data; Step 3.4: Calculate the correlation between the i-th group of data of any two sensors as follows: Where A and B are the codes of any two abnormal sensors; sim AB (i) is the spatial characteristic similarity of the i-th group of UHF data of two sensors A and B; is the normalized value of the jth eigenvalue of the i-th group of data of sensor A; is the normalized value of the jth eigenvalue of the i-th group of data of sensor B; Step 3.5: Calculate the mean similarity of n groups of UHF anomaly data of sensors A and B: Step 3.6: Abnormal sensor grouping: When When the abnormal data monitored by sensors A and B are judged to be related and come from the same discharge source; the abnormal sensors are grouped using the threshold of similarity, and the sensors that monitor the ultra-high frequency data of the same discharge source are grouped into one group; where S t is the similarity threshold.

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