Composite insulator electric field distortion detection method based on electroluminescent effect

Abstract

The invention provides a composite insulator electric field distortion detection method based on an electroluminescent effect, and the method comprises the steps: firstly, employing silicone rubber as a matrix material, optimizing a formula and a preparation technology, and preparing an electroluminescent composite coating material which is highly sensitive to electric field intensity and has excellent adhesiveness and environmental stability; secondly, the unmanned aerial vehicle is used for carrying a high-resolution camera, and comprehensive and clear insulator surface images are obtained in combination with the optimized route design; then, multivariable regression prediction is performed on image data through an algorithm, local and global characteristics of electric field distribution are accurately captured, and modeling errors are reduced. And finally, comparing an actually measured electric field distribution curve with a theoretical curve, analyzing electric field distortion, and diagnosing potential defects by using a feature matching technology. According to the invention, high detection accuracy can be maintained in different environments, the efficiency and precision of electric field distribution detection are greatly improved, and reliable technical support is provided for maintenance and safe operation of power equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment monitoring, and in particular to a composite insulator electric field distortion detection system based on electroluminescent effect and intelligent algorithm and an intelligent diagnosis method thereof. Background Art

[0002] With the further construction of my country's ultra-high voltage power grid project, more composite insulators will be used in high-voltage transmission lines. Due to design defects, material degradation, external environment and other factors, composite insulators may have defects and cause local electric field distortion, threatening the safe operation of the power system. Traditional electric field distortion detection technology mainly relies on the measurement of electric field strength by sensors, and then analyzes the electric field distribution through complex mathematical algorithms. However, the structure of composite insulators is complex, and there are many factors affecting the electric field distribution, such as surface contamination, mechanical stress, temperature change, etc., which makes traditional technology often unable to accurately capture the changes in electric field distortion, resulting in low monitoring accuracy. At the same time, the technology that relies on electric field sensors to detect the electric field distortion of composite insulators consumes a lot of manpower and has high requirements on the technical level of operators. Therefore, there is an urgent need for a simple, intuitive, and labor-saving composite insulator electric field distortion self-display technology. Summary of the invention

[0003] Based on the above-mentioned deficiencies of the prior art, the present invention realizes real-time monitoring of electric field distortion by introducing electroluminescent effect and white display technology, so as to timely discover and deal with potential faults of composite insulators, take corresponding maintenance measures, reduce further fault risks, and improve the reliability and safety of the power system. In addition, through intelligent monitoring means, remote monitoring and fault warning of the power system can be better realized, the intelligence level of the power system is improved, and the development and progress of the power industry is promoted.

[0004] The technical solution of the present invention is: a composite insulator electric field distortion detection method based on electroluminescence effect, comprising the steps of:

[0005] Step 1, preparing an electroluminescent composite coating material, including a base material and a fluorescent material, using silicone rubber as the base material and ZnS:Cu as the fluorescent material;

[0006] Step 2: Use a drone equipped with a high-resolution camera and an optimized flight path design to obtain a comprehensive and clear image of the insulator surface. Use an algorithm to predict the image data, accurately capture the local and global features of the electric field distribution, and reduce modeling errors.

[0007] Step 3: By comparing the actually measured electric field distribution curve with the theoretical curve, the electric field distortion is analyzed and the potential defects are diagnosed using feature matching technology.

[0008] Furthermore, in step 1, ZnS:Cu is used as the fluorescent material, and the specific preparation and coating process of the ZnS:Cu composite coating material is as follows: first, the required mass of silicone rubber material is weighed, poured into a flask and heated to 60°C to improve the fluidity of the matrix, a curing agent is added in proportion, and then the ZnS:Cu fluorescent material and barium titanate filler are added. Then, the heating at 60°C is maintained constant, stirring is continued for 1 hour, and then the mixture is placed in a vacuum drying oven for vacuuming until there are no bubbles on the surface. Finally, the ZnS:Cu composite coating material is coated on the surface of the cleaned and treated composite insulator by spraying to ensure that the coating thickness is uniform and complete. Bubbles and voids should be avoided during the coating process. Appropriate coating speed and coating thickness control methods are used to ensure the coating quality. After high-temperature curing, a composite insulator finally coated with the electroluminescent composite coating material is obtained.

[0009] Further, the specific process of step 2 is:

[0010] Image acquisition part: Use a drone equipped with a camera for shooting. Choose a suitable model of drone equipped with a high-resolution camera to ensure that clear and high-quality images can be obtained during the flight. At the same time, the camera can dynamically adjust the shooting angle. According to the pre-designed route, control the drone to fly autonomously, adjust the distance between the drone and the surface of the composite insulator, control the posture and camera angle, and take images of the surface of the composite insulator;

[0011] Image processing and analysis part: mainly based on the luminescent image to deduce the electric field distribution on the insulator surface to provide a basis for subsequent defect diagnosis. Based on the principle that the greater the field intensity, the greater the luminous intensity of the electroluminescent material, the relationship between field intensity and luminous intensity can be further obtained; first, the collected image is denoised to eliminate possible interference and improve image quality and clarity. Then, the image processing software is used to convert the preprocessed image into a grayscale image, and the grayscale value information of each pixel is extracted; finally, an electric field size model based on grayscale value is established, and the grayscale value of each pixel is converted according to the experimentally measured light intensity-electric field relationship to obtain the corresponding electric field size value.

[0012] Further, the specific steps of step 2 are as follows:

[0013] Step 2.1, Drone Settings

[0014] Choosing the right drone model is key to ensuring image acquisition quality. The following are specific selection criteria:

[0015] Flight stability: The drone should have good flight stability to reduce image blur caused by vibration or wind;

[0016] Payload capacity: The drone needs to have sufficient payload capacity to carry high-resolution cameras and other sensors;

[0017] Endurance: Choose a drone with a longer endurance to ensure that it can complete the image acquisition task for the entire route;

[0018] Therefore, the drones selected have high payload capacity and long flight time, and are equipped with RTK modules that can achieve centimeter-level flight accuracy;

[0019] Next, fix the high-resolution camera on the drone and make sure the camera can dynamically adjust the shooting angle. The specific installation steps are as follows:

[0020] Choose a high-resolution camera and use an anti-vibration bracket to firmly mount the camera on the bottom of the drone to ensure that it does not shake during flight; To ensure that the camera can dynamically adjust the shooting angle to meet different shooting needs, use a gimbal stabilizer to achieve this function;

[0021] Finally, the route is designed. According to the pre-designed route, the drone is controlled to fly autonomously, the distance between the drone and the surface of the composite insulator is adjusted, the attitude and camera angle are controlled, and the image of the surface of the composite insulator is captured. The specific steps are as follows:

[0022] Use flight planning software (DJI Terra) to pre-set the flight path to ensure that the drone can cover all composite insulator surfaces to be inspected;

[0023] Flight parameter settings: Flight altitude (H): Set the appropriate flight altitude according to the height of the insulator and the focal length of the camera; Shooting interval (I): Set the shooting interval to ensure that there is enough overlap between images. The recommended value is 3 seconds;

[0024] Autonomous flight: Start the drone's autonomous flight mode and fly along a predetermined route while dynamically adjusting the camera angle to obtain the best shooting effect;

[0025] Data monitoring: During the flight, the status of the drone and image acquisition are monitored in real time to ensure the accuracy and integrity of the data;

[0026] Step 2.2, image acquisition and preprocessing;

[0027] The drone flies along the predetermined route to capture images of the composite insulator surface. To ensure that the captured images can fully and accurately reflect the information of the composite insulator surface, the following settings are required:

[0028] Shooting angle: Set the pitch angle (θ) and yaw angle (φ) of the camera to obtain images at different angles;

[0029] Flight speed: Set the appropriate flight speed (v) to ensure image clarity;

[0030] Image capture: The drone flies according to the designed route and continuously captures images of the composite insulator surface during the flight;

[0031] Coverage: To ensure complete surface information, the image capture should cover all areas of the composite insulator, including the upper, middle and lower parts;

[0032] Image preprocessing includes denoising and grayscale conversion, aiming to improve image quality and extract useful information;

[0033] De-noising: De-noising the collected images to eliminate possible interference;

[0034] Among them, Gaussian filtering is a linear smoothing filtering method, which is often used to remove high-frequency noise in images. The formula of Gaussian filtering is:

[0035]

[0036] Where G(x, y) is the value of the Gaussian function at the point (x, y), which usually represents the value of the pixel value of the image at the point (x, y) after Gaussian filtering, x is the horizontal offset from the center of the Gaussian function, in pixels, y is the vertical offset from the center of the Gaussian function, in pixels, σ is the standard deviation, which determines the width of the Gaussian distribution (i.e., the degree of smoothness), and e is the base of the natural logarithm;

[0037] Grayscale conversion: Convert the preprocessed image into a grayscale image and extract the grayscale value information of each pixel. The grayscale value calculation formula is:

[0038] G(x, y)=0.299·R(x, y)+0.587·G(x, y)+0.114·B(x, y)

[0039] Among them, R(x, y), G(x, y), and B(x, y) are the red, green, and blue component values ​​of the pixel point (x, y) respectively;

[0040] Step,2.3, electric field distribution modeling;

[0041] The relationship between the grayscale value at different positions and the applied voltage is shown below:

[0042]

[0043] Among them, I, R, G, and B are the grayscale value, red value, green value, and blue value at different positions (x, y) under voltage U, respectively, and all have values ​​0-1.

[0044] The relationship between the electric field strength and the applied voltage at the calibration point is as follows:

[0045] E(x0, y0) = kU (2)

[0046] Among them, k is the corresponding coefficient, which is obtained from simulation and experimental data.

[0047] The relationship between the electric field intensity and the grayscale value at the calibration point is as follows:

[0048] E(x0,y0)=f(I(x0,y0,U)) (3)

[0049] Among them, f is the relationship function.

[0050] Therefore, the actual electric field strength can be inferred based on the grayscale value, as shown below:

[0051] E(x,y,U1)=f(I(x,y,U1)) (4)

[0052] Wherein, U1 is the corresponding voltage value at the position (x, y).

[0053] Further, the specific process of step 3 is:

[0054] Step 3.1, firstly, the electric field magnitude value of each pixel point is plotted into an electric field distribution map to intuitively display the electric field distribution on the surface of the composite insulator;

[0055] Step 3.2, then compare the actual predicted electric field distribution map with the theoretical electric field distribution, determine whether there is electric field distortion, the degree and range of distortion based on the difference analysis, match the electric field distortion position with the known electric field distortion characteristics, identify possible defect types and locations, including internal pores and partial discharge, and thus perform defect diagnosis;

[0056] Step 3.3, actuator operation: When electric field distortion or defects are detected, the system sends out an alarm signal, records the location and type of the defect, takes corresponding treatment measures according to the preset alarm level, and notifies maintenance personnel to carry out inspection and replacement.

[0057] Further, the specific steps of steps 3.2-3.3 are as follows:

[0058] Step 3.2, data preparation: grayscale image is converted into a graph structure. The preprocessed grayscale image data is converted into a graph structure. The node is each pixel in the image, and the edge is the connection relationship between adjacent pixels. If the image size is M×N, the number of nodes is M×N.

[0059] Graph structure representation:

[0060] G=(V,E)

[0061] Among them, V represents the node set, that is, the pixel points; E represents the edge set, that is, the relationship between adjacent pixel points;

[0062] Grayscale value conversion: In order to convert the grayscale value in the image into the corresponding electric field intensity value, it is necessary to use the experimentally measured light intensity-electric field relationship:

[0063] E(x0,y0)=f(I(x0,y0,U))

[0064] Next, the LSSVM-Adaboost model is used to train the above light intensity-electric field relationship;

[0065] Input feature, grayscale value: represents the brightness information of the pixel, denoted as I (x，y) ;

[0066] Pixel coordinates: represents the position of the pixel, recorded as (x, y);

[0067] Output data: target variable electric field intensity E;

[0068] LSSVM-Adaboost model introduction:

[0069] The objective function of the least squares support vector machine (LSSVM) is:

[0070]

[0071] Among them, w is the weight vector, b is the bias term, e is the error vector, γ is the regularization parameter, is the square of the deviation between the true value and the predicted value of sample i, N is the number of samples, and min is the minimum function;

[0072] Constraints:

[0073] y i =w T φ(x i )+b+e i , i=1,...,N

[0074] Among them, y i is the actual output value (true value) of the i-th sample, φ(x i ) represents the i-th sample x i feature map, i=1,...,N is the index of the sample;

[0075] Adaboost algorithm goal: iteratively adjust weights to enhance the model's attention to difficult-to-classify samples;

[0076] Weight update:

[0077]

[0078] Among them, w i is the sample weight at the i-th iteration, α i is the model weight at the i-th iteration, I is the indicator function, if the weak classifier h i The prediction result h for sample x i (x) is not equal to the true label y, then the function takes the value of 1, otherwise it takes the value of 0, h i is the weak classifier of the i-th iteration, y represents the true label of sample x,

[0079] LSSVM-Adaboost combined model, objective function and constraints:

[0080]

[0081] y i =w T φ(x i )+b+e i , i=1,…,N

[0082] Implementation steps:

[0083] Initialize weights:

[0084]

[0085] Training weak classifiers: For each iteration, use LSSVM to train a weak classifier and calculate the error e i And update the weights;

[0086] Model fusion: Use weighted voting to fuse the outputs of multiple weak classifiers;

[0087] Iterative update: Repeat the above process until the preset number of iterations or error threshold is reached;

[0088] After training, the model can predict the electric field strength value of each pixel in the image;

[0089] Distribution map comparison: The actual measured electric field distribution map is compared with the theoretical electric field distribution. The comparison method uses image difference calculation. This patent selects the mean square error (MSE):

[0090]

[0091] Where N is the total number of pixels in the image, E measured (i) is the actual measured electric field strength value of the i-th pixel, E theoretical (i) is the electric field intensity value of the i-th pixel in the theoretical model;

[0092] Defect identification: Match the electric field distortion position with the known electric field distortion features to identify possible defect types and locations, including internal pores and partial discharges;

[0093] Step 3.2, actuator operation;

[0094] Alarm signal: When electric field distortion or defects are detected, the system sends out an alarm signal and records the location and type of the defect. According to the preset alarm level, corresponding treatment measures are taken;

[0095] Actuator operation is a crucial part of the system, which ensures the precise execution of the UAV in image acquisition and electric field distribution detection through the automated control system;

[0096] The actuators include motors, servos, and camera gimbals, and the sensors include GPS modules, gyroscopes, accelerometers, and altimeters;

[0097] The formulas for adjusting the flight path and camera parameters are as follows:

[0098] v=v base +Δv

[0099] h=h base +Δh

[0100] θ=θ base +Δθ

[0101] ψ=ψ base +Δψ

[0102] Where v is the adjusted flight speed, h is the adjusted flight altitude, θ is the adjusted camera pitch angle, ψ is the adjusted heading angle, and v base 、h base ,θ base , base are the basic flight data of flight speed, flight altitude, camera pitch angle, and heading angle, respectively; Δv, Δh, Δθ, and Δψ are the flight speed, flight altitude, camera pitch angle, and heading angle adjusted according to the prediction results;

[0103] Real-time monitoring: Real-time monitoring of the drone’s flight status and image acquisition to ensure the accuracy and completeness of the data.

[0104] Beneficial effects:

[0105] 1. The present invention makes comprehensive use of the electroluminescent effect and self-display technology, and realizes real-time monitoring and defect diagnosis of electric field distortion by preparing specific luminescent composite coating materials. At the same time, by using a camera mounted on an unmanned aerial vehicle for image acquisition, combined with image processing technology, the electric field distribution information on the surface of the composite insulator can be quickly and accurately obtained. Finally, through the comparison of electric field distribution curves and defect diagnosis algorithms, automatic detection and alarm of electric field distortion of composite insulators can be achieved, providing reliable protection for the safe and stable operation of the power system. The present invention not only simplifies the operating procedures of electric field detection of composite insulators, lowers the technical threshold, saves human resources, and provides important support for the intelligent management of power systems, promoting the development and progress of the power industry.

[0106] 2. By introducing the LSSVM-Adaboost algorithm and combining the automated control system of the drone and the high-resolution camera, the present invention significantly improves the accuracy and stability of the modeling of the electric field distribution on the surface of the composite insulator. First, through the flexible route design and multi-angle shooting of the drone, comprehensive insulator surface image data is obtained to ensure the diversity and representativeness of the data. Secondly, the LSSVM-Adaboost algorithm is used to perform multivariate regression prediction on the image data, effectively capturing the local and global features in the electric field distribution and significantly reducing the modeling error. Experimental verification shows that the model can maintain a high prediction accuracy under different environmental conditions. In addition, the electric field distribution diagram after model optimization intuitively shows the electric field changes on the surface of the insulator, which helps to quickly identify electric field distortion and potential defects. Overall, the improvements of the present invention have greatly improved the efficiency and accuracy of electric field distribution detection, and provided reliable technical support for the maintenance and safe operation of power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0108] Figure 1 The specific preparation and coating process of ZnS:Cu composite coating material;

[0109] Figure 2 This is the flow chart of the intelligent detection system for composite insulator distorted electric field;

[0110] Figure 3 This is a flow chart of an intelligent feedback algorithm for data information of an intelligent power station according to the present invention. DETAILED DESCRIPTION

[0111] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the embodiments of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0112] The disclosure below provides many different embodiments or examples to implement different structures of the embodiments of the present invention. In order to simplify the disclosure of the embodiments of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the embodiments of the present invention. In addition, the embodiments of the present invention can repeat reference numbers and / or reference letters in different examples, and this repetition is for the purpose of simplification and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed.

[0113] In order to better understand the purpose, structure and function of the present invention, the following is a further detailed description of a composite insulator electric field distortion detection technology based on electroluminescence effect of the present invention in conjunction with the accompanying drawings.

[0114] like Figure 1-3 , combined with actual needs, the invention achieves the following purposes: First, to prepare an electroluminescent composite coating material with high sensitivity and stability, which can produce obvious luminous effect under the action of electric field, and has good adhesion, can be firmly coated on the surface of composite insulators, and has the characteristics of high temperature resistance, severe cold resistance, moisture resistance, etc., to meet the application needs of composite insulators under different environmental conditions. Secondly, it is necessary to develop a suitable image processing technology that can accurately reverse the electric field distribution according to the luminous signal generated by the material when the electric field is distorted, so as to realize the defect diagnosis of composite insulators. Finally, an online monitoring and alarm system is established, which can respond to changes in electric field distortion on the surface of composite insulators in a timely manner. When the monitoring device detects that the electric field distortion exceeds the preset threshold, it can automatically trigger the alarm mechanism and promptly notify relevant personnel to carry out inspection and maintenance, thereby avoiding potential failure risks.

[0115] 1. The composite insulator electric field distortion self-display technology based on the electroluminescent effect mainly includes two parts: the preparation and coating of electroluminescent composite coating materials and the composite insulator distorted electric field intelligent detection system.

[0116] 2. Further, the electroluminescent composite coating material mainly includes a matrix material and a fluorescent material. To ensure that the coating has good adhesion and environmental stability, the present invention uses a silicone rubber material as the matrix material; the fluorescent material is the core of the electroluminescent composite coating material, which is mostly a sulfide doped with metal ions. The present invention uses ZnS:Cu as the fluorescent material. Under different conditions, the ZnS:Cu material can emit two different colors of light. When copper ions replace zinc ions in the ZnS lattice, the material emits green light, and when copper ions are located in the lattice gap, the light color is blue. To ensure the luminous intensity and dielectric properties of the material, in the preparation of the electroluminescent composite coating material During the process, barium titanate is added as a supplementary filler. The essence of the luminescence phenomenon of the ZnS:Cu composite coating material is that the electric field affects the distribution of electron energy levels inside the material. Under the action of the electric field, a part of the electrons will be excited to a higher energy level to form an excited state. When these excited electrons return to the ground state, they will release energy and generate photons, thereby generating luminescence. According to the different carriers, the luminescence phenomenon is divided into two types, one is charge polarization luminescence, at which time the external electric field is lower than the discharge starting field strength, and the other is gas discharge induced luminescence, at which time the external electric field is higher than the discharge starting field strength, and the charge generated by the gas discharge accumulates on the coating surface, further emitting light in the form of radiation transition.

[0117] The specific preparation and coating process of ZnS:Cu composite coating material is as follows: First, weigh the required mass of silicone rubber material, pour it into a flask and heat it to 60°C to improve the fluidity of the matrix, add curing agent in proportion, and then add ZnS:Cu fluorescent material and barium titanate filler. Then, keep heating at 60°C and continue stirring for 1 hour. Then, put it into a vacuum drying oven for vacuum operation until there are no bubbles on the surface. Finally, spray the ZnS:Cu composite coating material on the surface of the cleaned and treated composite insulator to ensure that the coating thickness is uniform and complete. During the coating process, defects such as bubbles and gaps should be avoided. Appropriate coating speed and coating thickness control methods can be used to ensure the coating quality. After high-temperature curing, the composite insulator coated with the electroluminescent composite coating material is obtained.

[0118] 3. Further composite insulator distorted electric field intelligent detection system The composite insulator distorted electric field intelligent detection system includes three parts: image acquisition, image processing and analysis, and electric field distortion diagnosis;

[0119] The image acquisition part adopts the solution of using a drone equipped with a camera for shooting. The appropriate model of drone is selected, equipped with a high-resolution camera to ensure that clear and high-quality images can be obtained during the flight. At the same time, the camera can dynamically adjust the shooting angle, control the drone to fly autonomously according to the pre-designed route, adjust the distance between the drone and the surface of the composite insulator, control the posture and camera angle, and shoot images of the surface of the composite insulator;

[0120] The image processing and analysis part mainly derives the electric field distribution on the surface of the insulator based on the luminescent image to provide a basis for subsequent defect diagnosis. Based on the principle that the greater the field intensity, the greater the luminous intensity of the electroluminescent material, the relationship between the field intensity and the luminous intensity can be further obtained. First, the collected image is denoised to eliminate possible interference and improve image quality and clarity. Then, the image processing software is used to convert the preprocessed image into a grayscale image and extract the grayscale value information of each pixel. Finally, an electric field size model based on grayscale value is established. According to the experimentally measured light intensity-electric field relationship, the grayscale value of each pixel is converted to obtain the corresponding electric field size value.

[0121] The relationship between the grayscale value at different positions and the applied voltage is shown below:

[0122]

[0123] Among them, I, R, G, and B are the gray value, red value, green value, and blue value at different positions (x, y) under voltage U, and the values ​​are all 0-1;

[0124] The relationship between the electric field strength and the applied voltage at the calibration point is as follows:

[0125] E(x0,y0)=kU

[0126] Where k is the corresponding coefficient obtained from simulation and experimental data, and E is the electric field intensity at the calibration point;

[0127] The relationship between the electric field intensity and the grayscale value at the calibration point is as follows:

[0128] E(x0,y0)=f(I(x0,y0,U))

[0129] Where, f is the relationship function;

[0130] Therefore, the actual electric field strength can be inferred based on the grayscale value, as shown below:

[0131] E(x,y,U1)=f(I(x,y,U1))

[0132] The electric field distortion diagnosis part mainly includes the drawing of the electric field distribution curve on the insulator surface, curve analysis and comparison, and defect diagnosis. First, the electric field magnitude value of each pixel point is drawn into an electric field distribution diagram to intuitively display the electric field distribution on the composite insulator surface. Then, the actual measured electric field distribution diagram is compared with the theoretical electric field distribution. Based on the difference analysis, it is determined whether there is electric field distortion, as well as the degree and range of the distortion. The electric field distortion position is matched with the known electric field distortion characteristics, and the possible defect types and locations are identified, including internal pores and partial discharges, so as to perform defect diagnosis. When electric field distortion or defects are detected, the system sends an alarm signal, records the location and type of the defect, and takes corresponding treatment measures according to the preset alarm level, notifying maintenance personnel to carry out inspection and replacement.

[0133] 4. Furthermore, in order to more accurately diagnose electric field distortion and defects, a drone equipped with a high-resolution camera is used, combined with an optimized flight path design, to obtain comprehensive and clear images of the insulator surface; the image data is predicted through an algorithm to accurately capture the local and global characteristics of the electric field distribution and reduce modeling errors. The specific steps are as follows:

[0134] Step 1: Drone settings;

[0135] Choosing the right drone model is key to ensuring image acquisition quality. The following are specific selection criteria and models:

[0136] Flight stability: The drone should have good flight stability to reduce image blur caused by vibration or wind;

[0137] Payload capacity: The drone needs to have sufficient payload capacity to carry high-resolution cameras and other sensors;

[0138] Endurance: Choose a drone with a longer endurance to ensure that it can complete the image acquisition task for the entire route;

[0139] Therefore, the drone model selected is DJI Matrice 300RTK, which has high load capacity and long flight time, and is equipped with an RTK module, which can achieve centimeter-level flight accuracy;

[0140] Next, fix the high-resolution camera on the drone and make sure the camera can dynamically adjust the shooting angle. The specific installation steps are as follows:

[0141] The high-resolution camera chosen is the Sony Alpha 7R IV, which has a resolution of 61 megapixels and can provide clear images. The camera is firmly mounted on the bottom of the drone using an anti-vibration bracket to ensure that it does not shake during flight. To ensure that the camera can dynamically adjust the shooting angle to suit different shooting needs, a gimbal stabilizer is used to achieve this function.

[0142] Finally, the route is designed. According to the pre-designed route, the drone is controlled to fly autonomously, the distance between the drone and the surface of the composite insulator is adjusted, the attitude and camera angle are controlled, and the image of the surface of the composite insulator is captured. The specific steps are as follows:

[0143] Use flight planning software (DJI Terra) to pre-set the flight path to ensure that the drone can cover all composite insulator surfaces to be inspected;

[0144] Flight parameter settings: Flight altitude (H): Set the appropriate flight altitude according to the height of the insulator and the focal length of the camera; Shooting interval (I): Set the shooting interval to ensure that there is enough overlap between images. The recommended value is 3 seconds;

[0145] Autonomous flight: Start the drone's autonomous flight mode and fly along a predetermined route while dynamically adjusting the camera angle to obtain the best shooting effect;

[0146] Data monitoring: During the flight, the status of the drone and image acquisition are monitored in real time to ensure the accuracy and integrity of the data;

[0147] Step 2: Image acquisition and preprocessing;

[0148] The drone flies along the predetermined route to capture images of the composite insulator surface. To ensure that the captured images can fully and accurately reflect the information of the composite insulator surface, the following settings are required:

[0149] Shooting angle: Set the pitch angle (θ) and yaw angle (φ) of the camera to obtain images at different angles;

[0150] Flight speed: Set the appropriate flight speed (v) to ensure image clarity;

[0151] Image capture: The drone flies according to the designed route and continuously captures images of the composite insulator surface during the flight;

[0152] Coverage: To ensure complete surface information, the image capture should cover all areas of the composite insulator, including the upper, middle and lower parts;

[0153] Image preprocessing includes denoising and grayscale conversion, aiming to improve image quality and extract useful information;

[0154] De-noising: De-noising the collected images to eliminate possible interference;

[0155] Among them, Gaussian filtering is a linear smoothing filtering method, which is often used to remove high-frequency noise in images. The formula of Gaussian filtering is:

[0156]

[0157] Where G(x, y) is the value of the Gaussian function at the point (x, y), which usually represents the value of the pixel value of the image at the point (x, y) after Gaussian filtering, x is the horizontal offset from the center of the Gaussian function, in pixels, y is the vertical offset from the center of the Gaussian function, in pixels, σ is the standard deviation, which determines the width of the Gaussian distribution (i.e., the degree of smoothness), and e is the base of the natural logarithm;

[0158] Grayscale conversion: Convert the preprocessed image into a grayscale image and extract the grayscale value information of each pixel. The grayscale value calculation formula is:

[0159] G(x,y)=0.299·R(x,y)+0.587·G(x,y)+0.114·B(x,y)

[0160] Among them, R(x, y), G(x, y), and B(x, y) are the red, green, and blue component values ​​of the pixel point (x, y) respectively;

[0161] Step 3. First, data preparation: grayscale image is converted into a graph structure. The preprocessed grayscale image data is converted into a graph structure. The node is each pixel in the image, and the edge is the connection relationship between adjacent pixels. If the image size is M×N, the number of nodes is M×N.

[0162] Graph structure representation:

[0163] G=(V,E)

[0164] Among them, V represents the node set, that is, the pixel points; E represents the edge set, that is, the relationship between adjacent pixel points;

[0165] Grayscale value conversion: In order to convert the grayscale value in the image into the corresponding electric field intensity value, it is necessary to use the experimentally measured light intensity-electric field relationship:

[0166] E(x0,y0)=f(I(x0,y0,U))

[0167] Next, the LSSVM-Adaboost model is used to train the above light intensity-electric field relationship;

[0168] Input feature, grayscale value: represents the brightness information of the pixel, denoted as I (x，y) ;

[0169] Pixel coordinates: represents the position of the pixel, recorded as (x, y);

[0170] LSSVM-Adaboost model introduction:

[0171] The objective function of the least squares support vector machine (LSSVM) is:

[0172]

[0173] Among them, w is the weight vector, b is the bias term, e is the error vector, γ is the regularization parameter, is the square of the deviation between the true value and the predicted value of sample i, N is the number of samples, and min is the minimum function;

[0174] Constraints:

[0175] y i =w T φ(x i )+b+e i , i=1,…,N

[0176] Among them, y i is the actual output value (true value) of the i-th sample, φ(x i ) represents the i-th sample x i feature map, i=1,...,N is the index of the sample;

[0177] Adaboost algorithm goal: iteratively adjust weights to enhance the model's attention to difficult-to-classify samples;

[0178] Weight update:

[0179]

[0180] Among them, w i is the sample weight at the i-th iteration, α i is the model weight at the i-th iteration, I is the indicator function, if the weak classifier h i The prediction result h for sample x i (x) is not equal to the true label y, then the function takes the value of 1, otherwise it takes the value of 0, h i is the weak classifier of the i-th iteration, y represents the true label of sample x,

[0181] LSSVM-Adaboost combined model, objective function and constraints:

[0182]

[0183] y i =w T φ(x i )+b+e i , i=1,...,N

[0184] Implementation steps:

[0185] Initialize weights:

[0186]

[0187] Training weak classifiers: For each iteration, use LSSVM to train a weak classifier and calculate the error e i And update the weights;

[0188] Model fusion: Use weighted voting to fuse the outputs of multiple weak classifiers;

[0189] Iterative update: Repeat the above process until the preset number of iterations or error threshold is reached;

[0190] After training, the model can predict the electric field strength value of each pixel in the image;

[0191] Step 4: Generation and diagnosis of electric field distribution map;

[0192] Distribution diagram comparison: The actual measured electric field distribution diagram is compared with the theoretical electric field distribution. The comparison method uses image difference calculation. The present invention selects the mean square error (MSE):

[0193]

[0194] Where N is the total number of pixels in the image, E measured (i) is the actual measured electric field strength value of the i-th pixel, E theoretical (i) is the electric field intensity value of the i-th pixel in the theoretical model;

[0195] Defect identification: Match the electric field distortion position with the known electric field distortion features to identify possible defect types and locations, including internal pores and partial discharges;

[0196] Alarm signal: When electric field distortion or defects are detected, the system sends out an alarm signal and records the location and type of the defect. According to the preset alarm level, corresponding treatment measures are taken;

[0197] Step 5: Actuator operation;

[0198] Actuator operation is a crucial part of the system, which ensures the precise execution of the UAV in image acquisition and electric field distribution detection through the automated control system;

[0199] The actuators include motors, servos, and camera gimbals, and the sensors include GPS modules, gyroscopes, accelerometers, and altimeters;

[0200] The formulas for adjusting the flight path and camera parameters are as follows:

[0201] v=v base +Δv

[0202] h=h base +Δh

[0203] θ=θ base +Δθ

[0204] ψ=ψ base +Δψ

[0205] Where v is the adjusted flight speed, h is the adjusted flight altitude, θ is the adjusted camera pitch angle, ψ is the adjusted heading angle, and v base 、h base ,θ base , base are the basic flight data of flight speed, flight altitude, camera pitch angle, and heading angle, respectively; Δv, Δh, Δθ, and Δψ are the flight speed, flight altitude, camera pitch angle, and heading angle adjusted according to the prediction results;

[0206] Real-time monitoring: Real-time monitoring of the drone’s flight status and image acquisition to ensure the accuracy and completeness of the data.

[0207] Specific numerical examples:

[0208] Material preparation and coating process:

[0209] Base material: weigh 50g of silicone rubber material, put it into a flask and heat it to 60℃ to improve fluidity;

[0210] Additives: Add curing agent in proportion (10:1) and add 5g ZnS;

[0211] Fluorescent material and 2g barium titanate filler to ensure the luminescent performance and dielectric properties of the material;

[0212] Stirring and vacuuming: Keep heating at 60°C and continue stirring for 1 hour. Then place the mixed material in a vacuum drying oven and vacuumize until there are no bubbles on the surface;

[0213] Coating operation:

[0214] Surface treatment: Clean the surface of the composite insulator to ensure it is free of stains and grease;

[0215] Spraying: Use the spraying method to evenly coat the ZnS composite coating material on the surface of the insulator to ensure uniform coating thickness. Control the coating speed during the spraying process to avoid bubbles or gaps;

[0216] Curing: After coating, place the insulator in a high temperature environment of 150°C for 2 hours to ensure that the coating is firmly bonded to the substrate and the luminous performance is stable.

[0217] Electric field distortion diagnosis case:

[0218] At certain locations on the insulator surface, the predicted electric field strength is 130kV / m, while the theoretical electric field strength should be 100kV / m. By comparison, the electric field distortion is 30%. This area is shown as an area with abnormally high brightness on the electric field distribution map, and it is inferred that there may be internal pore defects at this location.

[0219] The electric field distortion information detected by the system is as follows:

[0220] Position: 10 cm from the top of the insulator, 2 cm offset to the left;

[0221] Electric field distortion degree: 30%;

[0222] Possible defect types: internal pores;

[0223] Alarm signal: Yellow alarm, prompting further inspection;

[0224] The LSSVM-Adaboost algorithm is used to predict the electric field intensity distribution on the surface of the composite insulator. The experimental design is as follows:

[0225] Equipment: Drone: DJI Phantom 4;

[0226] Camera: Canon EOS R5 (high-resolution camera, 45MP);

[0227] Data collection:

[0228] Route design: Set the drone flight altitude: 50 meters;

[0229] Predetermined route: The drone flies along the insulator surface within 30 meters, capturing images covering the entire surface;

[0230] Image acquisition: Take an image every 1 meter, and collect 50 images in total;

[0231] The resolution of the acquired images is 8192 × 5460 pixels;

[0232] Data preprocessing;

[0233] Image preprocessing: denoise each image and smooth it using a Gaussian filter;

[0234] Convert each image into a grayscale image and extract the grayscale value of each pixel;

[0235] LSSVM-Adaboost model training;

[0236] Input data: Feature selection: Gray value (G): the gray value of each pixel in the image; Pixel coordinates (x, y): the spatial position of each pixel;

[0237] Training data set: Each image is converted into a set of feature vectors (G, x, y);

[0238] There are 250,000 samples in total (50 images, 5000 pixels each);

[0239] Output data: Target variable: Electric field intensity (E): A linear regression model is established based on the relationship between light intensity and electric field measured experimentally. The range of electric field intensity is 0-10kV / m;

[0240] Model training and validation:

[0241] LSSVM-Adaboost model: Least Squares Support Vector Machine (LSSVM) is used as the base learner, and Adaboost is used to weightedly combine multiple LSSVM models;

[0242] Use 10-fold cross validation to evaluate model performance;

[0243] Specific parameters:

[0244] LSSVM parameters: C = 1 (penalty parameter);

[0245] γ = O.1 (kernel function parameter);

[0246] Adaboost parameters: Number of base learners: 50;

[0247] Result and effect analysis;

[0248] Model output: Electric field distribution map generated by electric field intensity prediction results. The predicted electric field intensity in a specific area (x=25, y=30) is 6.5kV / m. Accuracy assessment: By comparing with the actual measured electric field intensity (6.3kV / m), the prediction accuracy is 96%;

[0249] Effect summary:

[0250] Improve modeling accuracy: The LSSVM-Adaboost algorithm can effectively capture the local and global features of the electric field distribution, significantly improving the accuracy of electric field distribution modeling;

[0251] Real-time monitoring capabilities:

[0252] Through the automated control system, the model can adjust the flight path and camera angle of the drone in real time to obtain the best image data, reducing the errors caused by manual adjustments;

[0253] Fast defect identification:

[0254] The generated electric field distribution map can intuitively display the electric field distribution, quickly identify the location of electric field distortion and potential defects, and reduce the risk of equipment failure.

[0255] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A composite insulator electric field distortion detection method based on electroluminescence effect, characterized in that: Includes steps: Step 1, preparing an electroluminescent composite coating material, including a base material and a fluorescent material, using silicone rubber as the base material and ZnS:Cu as the fluorescent material; Step 2: Use a drone equipped with a high-resolution camera and an optimized flight path design to obtain a comprehensive and clear image of the insulator surface. Use an algorithm to predict the image data, accurately capture the local and global features of the electric field distribution, and reduce modeling errors. Step 3: By comparing the actually measured electric field distribution curve with the theoretical curve, the electric field distortion is analyzed and the potential defects are diagnosed using feature matching technology.

2. The method for detecting electric field distortion of composite insulators based on electroluminescence effect according to claim 1, characterized in that: In step 1, ZnS:Cu is used as the fluorescent material. The specific preparation and coating process of the ZnS:Cu composite coating material is as follows: first, weigh the required mass of silicone rubber material, pour it into a flask and heat it to 60°C to improve the fluidity of the matrix, add a curing agent in proportion, and then add the ZnS:Cu fluorescent material and barium titanate filler. Then, keep heating at 60°C and continue stirring for 1 hour. Then, enter the vacuum drying oven for vacuum operation until there are no bubbles on the surface. Finally, spray the ZnS:Cu composite coating material on the surface of the cleaned and treated composite insulator to ensure that the coating thickness is uniform and complete. During the coating process, bubbles and gap defects should be avoided. The coating speed and coating thickness control are adapted to ensure the coating quality. After high-temperature curing, the composite insulator finally coated with the electroluminescent composite coating material is obtained.

3. The method for detecting electric field distortion of composite insulators based on electroluminescence effect according to claim 1, characterized in that: The specific process of step 2 is: Image acquisition part: Use a drone equipped with a camera for shooting. Choose a suitable model of drone equipped with a high-resolution camera to ensure that clear and high-quality images can be obtained during the flight. At the same time, the camera can dynamically adjust the shooting angle. According to the pre-designed route, control the drone to fly autonomously, adjust the distance between the drone and the surface of the composite insulator, control the posture and camera angle, and take images of the surface of the composite insulator; Image processing and analysis part: First, the collected images are denoised to eliminate possible interference and improve image quality and clarity. Then, the preprocessed images are converted into grayscale images using image processing software, and the grayscale value information of each pixel is extracted. Finally, an electric field size model based on grayscale values ​​is established, and the grayscale value of each pixel is converted according to the experimentally measured light intensity-electric field relationship to obtain the corresponding electric field size value.

4. A composite insulator electric field distortion detection method based on electroluminescence effect according to claim 3, characterized in that: The specific steps of step 2 are as follows: Step 2.1, Drone Settings Choosing the right drone model is key to ensuring image acquisition quality. The following are specific selection criteria: Flight stability: The drone should have good flight stability to reduce image blur caused by vibration or wind; Payload capacity: The drone needs to have sufficient payload capacity to carry high-resolution cameras and other sensors; Endurance: Choose a drone with a longer endurance to ensure that it can complete the image acquisition task for the entire route; Therefore, the drones selected have high payload capacity and long flight time, and are equipped with RTK modules that can achieve centimeter-level flight accuracy; Next, fix the high-resolution camera on the drone and make sure the camera can dynamically adjust the shooting angle. The specific installation steps are as follows: Choose a high-resolution camera and use an anti-vibration bracket to firmly mount the camera on the bottom of the drone to ensure that it does not shake during flight; To ensure that the camera can dynamically adjust the shooting angle to meet different shooting needs, use a gimbal stabilizer to achieve this function; Finally, the route is designed. According to the pre-designed route, the drone is controlled to fly autonomously, the distance between the drone and the surface of the composite insulator is adjusted, the attitude and camera angle are controlled, and the image of the surface of the composite insulator is captured. The specific steps are as follows: Use flight planning software to pre-set the flight path to ensure that the drone can cover all composite insulator surfaces to be inspected; Flight parameter settings: Flight altitude H: Set the appropriate flight altitude according to the height of the insulator and the focal length of the camera; Shooting interval I: Set the shooting interval to ensure that there is enough overlap between images; Autonomous flight: Start the drone's autonomous flight mode and fly along a predetermined route while dynamically adjusting the camera angle to obtain the best shooting effect; Data monitoring: During the flight, the status of the drone and image acquisition are monitored in real time to ensure the accuracy and integrity of the data; Step 2.2, image acquisition and preprocessing; The drone flies along the predetermined route to capture images of the composite insulator surface. To ensure that the captured images can fully and accurately reflect the information of the composite insulator surface, the following settings are required: Shooting angle: Set the pitch angle θ and yaw angle φ of the camera to obtain images at different angles; Flight speed: Set an appropriate flight speed v to ensure image clarity; Image capture: The drone flies according to the designed route and continuously captures images of the composite insulator surface during the flight; Coverage: To ensure complete surface information, the image capture should cover all areas of the composite insulator, including the upper, middle and lower parts; Image preprocessing includes denoising and grayscale conversion, aiming to improve image quality and extract useful information; De-noising: De-noising the collected images to eliminate possible interference; Among them, Gaussian filtering is a linear smoothing filtering method, which is often used to remove high-frequency noise in images. The formula of Gaussian filtering is: Where G(x, y) is the value of the Gaussian function at the point (x, y), which usually represents the value of the pixel value of the image at the point (x, y) after Gaussian filtering, x is the horizontal offset from the center of the Gaussian function, in pixels, y is the vertical offset from the center of the Gaussian function, in pixels, σ is the standard deviation, which determines the width of the Gaussian distribution, and e is the base of the natural logarithm; Grayscale conversion: Convert the preprocessed image into a grayscale image and extract the grayscale value information of each pixel. The grayscale value calculation formula is: G(x, y)=0.299·R(x, y)+0.587·G(x, y)+0.114·B(x, y) Among them, R(x, y), G(x, y), and B(x, y) are the red, green, and blue component values ​​of the pixel point (x, y) respectively; Step,2.3, electric field distribution modeling; The relationship between the grayscale value at different positions and the applied voltage is shown below: Among them, I, R, G, and B are the gray value, red value, green value, and blue value at different positions (x, y) under voltage U, and the values ​​are all 0-1; The relationship between the electric field strength and the applied voltage at the calibration point (x0, y0) is shown below: E(x0,y0)=kU Among them, k is the corresponding coefficient, which is obtained from simulation and test data; The relationship between the electric field intensity and the grayscale value at the calibration point is as follows: E(x0,y0)=f(I(x0,y0,U)) Where, f is the relationship function; Therefore, the actual electric field strength can be inferred based on the grayscale value, as shown below: E(x,y,U1)=f(I(x,y,U1)) Wherein, U1 is the corresponding voltage value at the position (x, y).

5. The method for detecting electric field distortion of composite insulators based on electroluminescence effect according to claim 1, characterized in that: The specific process of step 3 is: Step 3.1, firstly, the electric field magnitude value of each pixel point is plotted into an electric field distribution map to intuitively display the electric field distribution on the surface of the composite insulator; Step 3.2, then compare the actual predicted electric field distribution map with the theoretical electric field distribution, determine whether there is electric field distortion, the degree and range of distortion based on the difference analysis, match the electric field distortion position with the known electric field distortion characteristics, identify possible defect types and locations, including internal pores and partial discharge, and thus perform defect diagnosis; Step 3.3, actuator operation: When electric field distortion or defects are detected, the system sends out an alarm signal, records the location and type of the defect, takes corresponding treatment measures according to the preset alarm level, and notifies maintenance personnel to carry out inspection and replacement.

6. A composite insulator electric field distortion detection method based on electroluminescence effect according to claim 5, characterized in that: The specific steps of steps 3.2-3.3 are as follows: Step 3.2, data preparation: grayscale image is converted into a graph structure. The preprocessed grayscale image data is converted into a graph structure. The node Node is each pixel in the image, and the edge Edge is the connection relationship between adjacent pixels. If the image size is M×N, the number of nodes is M×N. The graph structure G represents: G=(V,E) Among them, V represents the node set, that is, the pixel points; E represents the edge set, that is, the relationship between adjacent pixel points; Grayscale value conversion: In order to convert the grayscale value in the image into the corresponding electric field intensity value, it is necessary to use the experimentally measured light intensity-electric field relationship: E(x0,y0)=f(I(x0,y0,U)) Next, the least squares support vector machine-adaptive boosting algorithm LSSVM-Adaboost model is used to train the above light intensity-electric field relationship; Input feature, grayscale value: represents the brightness information of the pixel, denoted as I (x，y) ; Pixel coordinates: represents the position of the pixel, recorded as (x, y); Output data: target variable electric field intensity E; Training weak classifiers: For each iteration, use LSSVM to train a weak classifier and calculate the error e i And update the weights; Model fusion: Use weighted voting to fuse the outputs of multiple weak classifiers; Iterative update: Repeat the above process until the preset number of iterations or error threshold is reached; After training, the model can predict the electric field strength value of each pixel in the image; Distribution map comparison: The actual predicted electric field distribution map is compared with the theoretical electric field distribution. The comparison method uses image difference calculation. The present invention selects the mean square error MSE: Where N is the total number of pixels in the image, E measured (i) is the actual measured electric field strength value of the i-th pixel, E theoretical (i) is the electric field intensity value of the i-th pixel in the theoretical model; Defect identification: Match the electric field distortion position with the known electric field distortion features to identify possible defect types and locations, including internal pores and partial discharges, to perform defect diagnosis; Step 3.3, Actuator Operation Alarm signal: When electric field distortion or defects are detected, the system sends out an alarm signal and records the location and type of the defect. According to the preset alarm level, corresponding treatment measures are taken; Actuator operation is a crucial part of the system, which ensures the precise execution of the UAV in image acquisition and electric field distribution detection through the automated control system; The actuators include motors, servos, and camera gimbals, and the sensors include GPS modules, gyroscopes, accelerometers, and altimeters; The formulas for adjusting the flight path and camera parameters are as follows: v=v base +Δv h=h base +Δh θ=θ base +Δθ ψ=ψ base +Dψ Where v is the adjusted flight speed, h is the adjusted flight altitude, θ is the adjusted camera pitch angle, ψ is the adjusted heading angle, and v base 、h base ,θ base , base are the basic flight data of flight speed, flight altitude, camera pitch angle, and heading angle, respectively; Δv, Δh, Δθ, and Δψ are the flight speed, flight altitude, camera pitch angle, and heading angle adjusted according to the prediction results; Real-time monitoring and processing: Real-time monitoring of the UAV’s flight status and image acquisition to ensure the accuracy and completeness of the data, record the location and type of defects, take appropriate processing measures based on the preset alarm level, and notify maintenance personnel to carry out inspection and replacement.

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