A method, system and device for detecting discharge of an insulator based on a ternary color space

Abstract

The application discloses a discharge detection method for insulators based on a ternary color space, and has the characteristics that the method comprises the following steps: collecting abnormal discharge of insulators by using a four-channel light quantum detection device to obtain four-channel spectral data; extracting RGB spectral features from RGB band spectral data in the four-channel spectral data respectively, and performing principal component analysis on full-band spectral data in the four-channel spectral data based on the RGB spectral features to obtain three principal components; calculating the correlation between the principal components and the RGB spectral features by using the load of the RGB spectral features in each principal component to obtain correlation coefficients; implementing adjustment on the RGB band spectral data collected by the RGB spectral channel through the correlation coefficients to update the RGB band spectral data, and detecting the discharge type of the insulators based on spectral features in the RGB spectral data.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power systems, and more particularly, to a discharge detection method, system and device for insulators based on a ternary color space. BACKGROUND

[0002] In the external insulation of power systems, contamination and aging are the two main factors leading to abnormal discharge. Contamination refers to the accumulation of dust, salt, chemicals and other impurities on the surface of insulating materials. These impurities can form a conductive path in a humid environment, leading to a decrease in the insulating properties of the insulating material. Aging refers to the degradation of material properties due to long-term exposure to ultraviolet light, thermal stress, oxidation and other environments, thereby reducing its insulating properties. Both of these factors can lead to partial discharge, which is the occurrence of local electric field concentration inside or on the surface of the insulating material, causing discharge. By detecting and judging the type and discharge stage of abnormal discharge, the contamination and aging state of the external insulation can be reflected to some extent, so how to detect the type and discharge stage of abnormal discharge is particularly important for the safe operation of power equipment.

[0003] The performance of the photoelectric sensor is the key to effectively detecting weak discharge light. A silicon photomultiplier (SiPM) integrates thousands of avalanche photodiodes (APDs), each of which forms a dissipation zone capable of accepting photons through a large-area p-n junction based on silicon doping. When the field strength reaches 5x10 5 V / cm, the electron-hole pairs in it gain enough kinetic energy to trigger a secondary avalanche breakdown, a process known as Geiger avalanche; SiPM integrates thousands of APDs on a mm 2 scale and uses an avalanche-quenching-charging switching process to achieve photon counting. Under a 25+2.5 V bias, a 6 mm 2 size SiPM (dead zone ratio <10%) has a quantum efficiency of up to 50% and excellent anti-magnetic interference performance. In summary, SiPM can effectively detect weak light signals and can be applied to abnormal discharge light measurement.

[0004] Combining the theory of spectral detection and the availability of silicon photomultipliers, there is an urgent need for a discharge detection method, system and device for insulators based on a ternary color space. SUMMARY

[0005] To solve the problems in the prior art, the present application provides a discharge detection method, system and device for insulators based on a ternary color space, which extracts four-channel SiPM spectral features through principal component analysis.

[0006] The present application adopts the following technical solutions.

[0007] The first aspect of the present application relates to a method for detecting discharge of an insulator based on a ternary color space, the method comprising the following steps: collecting abnormal discharge of the insulator by using a four-channel light quantum detection device to obtain four-channel spectral data; extracting RGB spectral features from RGB band spectral data in the four-channel spectral data respectively, and performing principal component analysis on full-band spectral data in the four-channel spectral data based on the RGB spectral features to obtain three principal components; calculating the correlation between the principal components and the RGB spectral features by using the load of the RGB spectral features in each principal component to obtain correlation coefficients; implementing adjustment on the RGB band spectral data collected by the RGB spectral channel through the correlation coefficients to update the RGB band spectral data, and detecting the discharge type of the insulator based on spectral features in the RGB spectral data.

[0008] Preferably, the RGB spectral features are extracted from the RGB band spectral data in the four-channel spectral data, including: collecting spectral intensity values of each APD in the RGB channel by the light quantum detection device, converting the spectral intensity values into gray values, summarizing the gray values of all APDs to obtain a gray matrix of any RGB channel as the RGB spectral feature.

[0009] Preferably, the principal component analysis is performed on the full-band spectral data in the four-channel spectral data based on the RGB spectral features to obtain three principal components, including: collecting the RGB spectral features and the full-band spectral features in the RGB channel under a plurality of continuous time periods or a plurality of APD sensor signals , is a sample number, respectively, the RGB spectral features and the full-band spectral features; taking the spectral features under a plurality of continuous time periods as input and taking the sum of variances of the secondary components as the target, the first to third principal components of the full-band spectral features are obtained by using the principal component analysis method as the decomposition result.

[0010] Preferably, the correlation between the principal components and the RGB spectral features is calculated by using the load of the RGB spectral features in each principal component to obtain the correlation coefficients, including: according to the coordinates of the load of the first to third principal components in the principal component space , in the formula, and respectively, the RGB three-channel number and the principal component coordinate axis number; taking the coordinates of the load in the principal component space as the correlation coefficients.

[0011] ​Preferably, the RGB band spectrum data collected by the correlation coefficient is adjusted to update the RGB band spectrum data, including: using the RGB band spectrum decomposed from the first to third principal components as the adjusted RGB band spectrum data; or using the intensity difference of the RGB band spectrum decomposed from the first to third principal components to adjust the RGB band spectrum weight of the summary spectrum; the adjustment method is:

[0012]

[0013] wherein, , and are the adjusted RGB band spectrum data.

[0014] Preferably, the summary spectrum is:

[0015]

[0016] and, , , are determined by the intensity difference of , and .

[0017] Preferably, the discharge type of the insulator is detected based on the spectral characteristics in the RGB spectrum data, including: obtaining the preliminary discharge type based on the intensity difference , , , and the intensity difference of any discharge type is obtained by pre-computation; under the preset discharge voltage, the probability distribution of the number of light pulses in the gray value dimension in the summary spectrum matrix is counted; the current discharge type is determined by using the preliminary discharge type and the probability distribution of the number of light pulses.

[0018] Preferably, the discharge type of the insulator is detected based on the spectral characteristics in the RGB spectrum data, including: collecting the number of light pulses with a gray level of 0 in the channel matrix to calculate the pre-breakdown value; the breakdown time of the current discharge is determined based on the pre-breakdown value.

[0019] In a second aspect, the application relates to a discharge detection system for insulators based on a ternary color space, which is implemented by using the method in the first aspect; the system comprises a collection module, an analysis module, a correlation module and a detection module; the collection module is used to collect abnormal discharge of insulators by using a four-channel light quantum detection device to obtain four-channel spectral data; the analysis module is used to extract RGB spectral features from RGB band spectral data in the four-channel spectral data, and perform principal component analysis on full-band spectral data in the four-channel spectral data based on the RGB spectral features to obtain three principal components; the correlation module is used to calculate the correlation between the principal components and the RGB spectral features by using the load of the RGB spectral features in each principal component, so as to obtain correlation coefficients; and the detection module is used to implement adjustment on the RGB band spectral data collected by the RGB spectral channel based on the correlation coefficients, to update the RGB band spectral data, and detect the discharge type of the insulators based on the spectral features in the RGB spectral data.

[0020] In a third aspect, the application relates to a photoelectric sensing device, which is composed of four sensing channels; each sensing channel comprises a front-end light guide, a light filtering unit, a photoelectric sensing array, a signal preprocessing unit, a signal collection and calculation unit, a communication unit, a power management module and a display and alarm unit; the front-end light guide and the light filtering unit receive light path signals; the photoelectric sensing array, the signal preprocessing unit, the signal collection and calculation unit, the communication unit, the power management module and the display and alarm unit realize discharge detection and alarm in the discharge detection method based on a ternary color space.

[0021] The application has the following beneficial effects: compared with the prior art, the application provides a discharge detection device and a diagnosis method based on a ternary color space, which has high anti-interference ability and signal-to-noise ratio, can judge the discharge state, and provides effective early discharge fault detection and diagnosis criteria.

[0022] The application has the following beneficial effects:

[0023] 1. The light quantum device is used for discharge detection, which improves the confidence and defect detection ability of discharge detection. The detection device provided by the application has simple structure and is easy to install, and can be deployed at a position where external insulation failure is prone to occur, so that fault positioning and troubleshooting are facilitated. The detection method provided by the application is simple and fast, and facilitates rapid discharge detection.

[0024] 2. Low-energy discharge is mainly in the form of ultraviolet radiation, and high-energy discharge is mainly in the form of infrared radiation. The discharge energy level and risk degree can be quantitatively analyzed by using the ternary color space based on the above principle. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1A schematic diagram of the structure of the insulator discharge detection device based on a ternary color space according to the present application;

[0026] Figure 2 A probability distribution curve of three types of typical discharge light pulse gray scales in the insulator discharge detection method based on a ternary color space according to the present application;

[0027] Figure 3 A schematic diagram of P value changes of three types of typical discharge pulses in the insulator discharge detection method based on a ternary color space according to the present application. DETAILED DESCRIPTION

[0028] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below with reference to the accompanying drawings of the embodiments of the present application. The embodiments described in the present application are only a part of the embodiments of the present application, rather than all the embodiments. Based on the spirit of the present application, all other embodiments not described in the present application obtained by those skilled in the art without creative work according to the embodiments described in the present application should belong to the protection scope of the present application.

[0029] Generally, abnormal discharge is active in a very limited area in space, so it can be regarded as a point light source. Photons emitted by the light source can be coupled by different spectral sensitive areas in the photoelectric sensing device at the same time, so that multi-spectral light signal synchronous acquisition can be realized. In addition to the traditional abnormal discharge phase analysis (PRPD), spectral information provides an additional physical dimension for abnormal discharge diagnosis.

[0030] The present application adopts a silicon photomultiplier (SiPM) to design a photoelectric sensing device. The device includes a front-end light guide, a light filtering unit, a photoelectric sensing array, a signal preprocessing unit, a signal acquisition and calculation unit, a communication unit, a power management module and a display alarm unit.

[0031] The front-end light guide and the light filter unit belong to the optical part and receive the same optical path signal, the front-end light guide collects and conducts the abnormal discharge light signal, the light signal obtains different waveband signals through the light filter unit, and the front-end light guide and the light filter unit are arranged at the input end of the photoelectric device. The photoelectric sensing array, the signal preprocessing unit, the signal acquisition and calculation unit, the communication unit, the power management module and the display alarm unit belong to the circuit part; the optical part and the circuit part are independent, and the modular design makes it convenient to install and more flexible in application. The photoelectric sensing array is used for photoelectric signal conversion of the multiple light beams obtained through the front-end light guide and the light filter unit, converts the light signal into a current signal, and outputs multiple analog current signals; the signal preprocessing unit is used for processing the analog current signal output by the photoelectric sensing array, and the module includes an (transimpedance) I-U conversion unit, a filter unit, a voltage following amplification unit and a frequency reduction detection unit, each unit works in a cascading manner, and outputs multiple analog voltage signals. The signal acquisition and calculation unit is used for acquisition, calculation and display of the analog voltage signal output by the signal preprocessing unit. The power management module is used for power supply of the photoelectric sensing array, the boost module, the signal preprocessing unit, the signal acquisition and calculation unit and the communication unit. The communication unit is used for transmitting the result signal output by the signal acquisition and calculation unit to the upper computer. The display alarm unit is controlled by the signal acquisition and calculation unit, and is used for displaying the discharge energy level, light flickering and buzzer alarm. The functions of the above units are combined, and the photoelectric sensing device realizes acquisition, transmission and processing of the abnormal discharge light signal.

[0032] The photoelectric sensing array of the device adopts an avalanche diode area array (MicroFJ 60035 The TSV is composed of four single SiPMs with a side length of 6 mm, the operating temperature interval is -40 ℃ to +85 ℃, the maximum current is 10 mA, and the basic structure of the photoelectric sensing device is shown in Fig. 1. Figure 1 The key parameters are shown in Table 1.

[0033] Table 1 Device technical parameters

[0034]

[0035] According to the R, G and B waveband specifications, the filter waveband is selected, the filter group selects three waveband bandpass filters with the same area as the front-end avalanche diode area array, covers the main spectral range generated by gas discharge, and the specific parameters are shown in Table 2.

[0036] Table 2 Filter technical parameters

[0037]

[0038] The analog board realizes the function of a signal preprocessing unit, and an input negative bias voltage is 27.5 V. The digital board realizes the functions of the above-mentioned signal acquisition and calculation unit and communication unit, adopts an ultra-high-performance microcontroller with a type of AT32 (a process of 55 nm, a main frequency of 288 MHZ), a working temperature of-40 DEG C to 105 DEG C, and a digital sampling rate of 3 MS / s.

[0039] The four SiPM chips of the device respectively receive R, G and B three-band spectrum signals and full-band spectrum signals.

[0040] The first aspect of the present application relates to a kind of based on three-element color space insulator discharge detection method, method includes the following steps: using four-channel light quantum detection device collection insulator abnormal discharge, to obtain four-channel spectrum data;From the RGB waveband spectrum data in four-channel spectrum data respectively extract RGB spectrum feature, and with the RGB spectrum feature as the basis, the principal component analysis is carried out to the full-band spectrum data in four-channel spectrum data, to obtain three principal components;The correlation between the principal component and the RGB spectrum feature is obtained by using the load of the RGB spectrum feature in each principal component, to obtain correlation coefficient;The RGB waveband spectrum data collected by the RGB spectrum channel is adjusted by the correlation coefficient, to update the RGB waveband spectrum data, and the discharge type of insulator is detected based on the spectrum feature in RGB spectrum data.

[0041] The four SiPM chips of the device respectively receive R, G and B three-band spectrum signals and full-band spectrum signals.

[0042] The RGB spectrum feature is extracted from the RGB waveband spectrum data in four-channel spectrum data, including: the spectral intensity value of each APD in RGB channel is collected by light quantum detection device, the spectral intensity value is converted into gray value, the gray value of all APDs is summarized, the gray matrix of any one RGB channel is obtained, and this is used as RGB spectrum feature.

[0043] The collected full-band spectral data and the spectral data of the RGB band are standardized as the basis of PCA analysis, ensuring that the data of each band has zero mean and unit variance, which is an important prerequisite for PCA analysis. Then, the PCA algorithm is used to decompose the standardized full-band data. The PCA algorithm converts possibly correlated variables into a set of linearly uncorrelated variables, called principal components, through orthogonal transformation. These principal components are sorted according to the size of the variance, and the first principal component has the largest variance, capturing the most information in the data set, and the subsequent components contain less and less information.

[0044] Based on the RGB spectral features, principal component analysis is performed on the full-band spectral data in the four-channel spectral data to obtain three principal components, including: collecting a plurality of continuous time periods The RGB spectral features and the full-band spectral features of the APD sensor signals under the plurality of APD sensor signals , is a sample number, respectively, the RGB spectral features, the full-band spectral features; taking the spectral features in the plurality of continuous time periods as input, taking the sum of the variances of the secondary components as the target, and using the principal component analysis method to obtain the decomposition results of the first to third principal components of the full-band spectral features .

[0045] After the principal component analysis conversion, the principal component loading diagram needs to be analyzed in detail to determine the specific relationship between each band and the principal component. The principal component loading diagram is a graphical tool that shows the correlation between the RGB band spectral data and each principal component.

[0046] The correlation between the principal component and the RGB spectral feature is calculated using the load of the RGB spectral feature in each principal component, thereby obtaining the correlation coefficient, including: according to the coordinates of the loads of the first to third principal components in the principal component space , wherein, and are respectively the RGB three-channel number and the principal component coordinate axis number; and the coordinates of the loads in the principal component space as the correlation coefficient.

[0047] In a specific band range, those bands with higher load values contribute more significantly to the formation of the first principal component (PC1), and they are more critical in forming PC1 compared to surrounding bands. Through principal component decomposition, independent features are obtained in the RGB band, which show potential independence from other bands in continuous spectra. These independent features are the most representative part of the discharge state response in spectral data, and therefore have important significance for discharge detection and diagnosis.

[0048] The RGB band spectrum data collected by the RGB spectrum channel is adjusted by the correlation coefficient to update the RGB band spectrum data, including: using the RGB band spectrum decomposed from the first to third principal components as the adjusted RGB band spectrum data; or adjusting the RGB band spectrum weight of the summary spectrum using the intensity difference of the RGB band spectrum decomposed from the first to third principal components; and the adjustment method is:

[0049]

[0050] wherein, , and are the adjusted RGB band spectrum data.

[0051] After the PCA model is established, the device designed by the application is used to collect spectrum information of any partial discharge, a part of the data is used to train the model to obtain the value of the coefficient matrix, and another part of the data is used to verify the accuracy of the model. By comparing the R, G and B band data predicted by the model with the actual measured values, the performance of the model is evaluated. The calibrated model obtained by verification is applied to the entire data set, and the principal components are used to adjust the original R, G and B band data to eliminate bias and improve the consistency of the data. This process can reduce errors in the data and improve the accuracy and reliability of the spectrum data.

[0052] The summary spectrum is:

[0053]

[0054] and, , , determined by the intensity difference of , and

[0055] The brightness of different discharge intensities is not the same, and the gray scale and chroma information of the spectrum can reflect the microscopic process of the discharge. A unified brightness index is usually represented by the total gray value L, which is obtained by different weighting of the brightness values of the three color channels (i.e. ternary gray values). In this way, L represents the overall brightness of the picture, and the numerical value is also 0-255. At the same time, considering the difference in sensitivity of the three color gray scales in gas discharge light radiation to reflect discharge, the ternary gray scale can be linearly weighted to form L.

[0056] Detecting the discharge type of the insulator based on the spectral characteristics in the RGB spectrum data, including: detecting the discharge type of the insulator based on the intensity difference , , ​The initial discharge type is obtained, and the intensity difference of any discharge type is pre-calculated; under a preset discharge voltage, the spectral matrix is ​​statistically summarized. The probability distribution of the number of light pulses in the grayscale dimension; the current discharge type is determined by the probability distribution of the initial discharge type and the number of light pulses.

[0057] By counting the number of light pulses at each gray level under a certain discharge voltage and then dividing by the total number of light pulses, the distribution of each gray level from 0 to 255 can be obtained.

[0058]

[0059] In the formula, At discharge voltage grayscale The probability distribution of light pulses under the following conditions;

[0060] At discharge voltage grayscale The number of light pulses;

[0061] At discharge voltage The total number of light pulses at all gray levels, and having

[0062]

[0063] like Figure 2 The gray level with the highest probability represents the number of light pulses at that gray level in the entire cycle. The steeper the curve, the more light pulses with the highest probability there are, and the more uniform the brightness. The smoother the curve, the more pulse points with different brightness levels are distributed.

[0064] In the embodiments, gray-scale probability distribution curves of three typical discharge light pulses obtained using the above-mentioned measuring device are given, with the statistical time of the light pulses at each voltage being 5.6s. The overall brightness of corona discharge is relatively uniform, with the highest probability gray level appearing around 10-13, and the proportion increasing with the increase of voltage level; the gray-scale distribution of surface discharge is more dispersed than that of corona discharge. At lower voltage levels, the highest gray level appears around 5-8, and the highest gray level shows an increasing trend with the increase of voltage level. At 18kV, the frequency of the highest gray level increases to 10-15. The gray-scale distribution of suspension discharge is the most concentrated, generally distributed between 5-18. In the early stage of discharge development, the highest gray-scale frequency appears around 5-10. With the increase of voltage, the gray-scale frequency develops to the right. After the voltage develops to 19kV, the highest gray-scale frequency appears around 13-16.

[0065] Due to discharge uncertainty and light intensity is too high at the breakdown moment, a large number of light pulses and B-band light intensity exceeding the measurement range are caused, and the following method is given in the application.

[0066] The B-component 0 gray level frequency in the region is extracted and named as P value method. As an index, the sensitivity to the initial stage of discharge is increased, and the problem of B-component saturation in the pre-breakdown stage is effectively avoided.

[0067]

[0068] In the formula, P is the frequency of the B-component gray level being 0, is the number of light pulses with the B-component gray level being 0 in the calculation region, is the total number of light pulses in the measurement range under a voltage.

[0069] Figure 3 Three typical light pulse P value embodiments with voltage variation are given. As shown in the figure, with the increase of the degree of discharge intensity, the P values of the three types of discharge faults are all decreased, and the overall evolution law presents the "opposite nature" with the average gray value. Taking the surface discharge as an example for main analysis, in the initial corona stage, the discharge is mainly concentrated near the high-voltage pole, the photon range is small, and therefore the P value is relatively large; in the discharge development stage, the P value rapidly decreases; when the discharge develops to the pre-breakdown stage, the discharge emits fierce "puff puff" sound, at this time, the discharge phenomenon is relatively intense, and the P value is reduced to about 0.4; then the pressure continues to increase, and the discharge occurs breakdown. The P values of the suspension discharge and the surface discharge are both between 0.4 and 0.5 in the critical breakdown region.

[0070] Detecting the discharge type of the insulator based on the spectral characteristics in the RGB spectral data, comprising: acquiring a channel matrix The number of light pulses with the lower gray level being 0 is counted to calculate the pre-breakdown value; and the breakdown moment of the current discharge is judged based on the pre-breakdown value.

[0071] In the ternary chroma space provided by the application, the gray level frequency distribution curve reflects the discharge light radiation energy distribution, and the highest frequency gray level P value gradually decreases with the discharge light radiation energy. The preferred criterion of the application is: initial discharge, P∈[0.7, 1]; medium-high energy discharge, P∈[0.45, 0.7); and intense discharge, P∈[0.45, 0]. If the P value curve is used, the discharge energy can be more accurately tracked and identified, and is not affected by the background color and the material.

[0072] For the criterion of discharge type: suspension discharge: when the voltage is low (6kV to 10kV), the P value is high. After exceeding 10kV, the P value decreases. Surface discharge: the P value is relatively stable between 8kV to 10kV, and the P value decreases when the voltage is lower than 8kV or higher than 10kV. Corona discharge: the P value reaches the peak at the voltage of 8kV. When the voltage is lower than 8kV or higher than 10kV, the P value decreases.

[0073] According to the above-mentioned various criteria, the present application comprehensively analyzes the discharge type and discharge breakdown time, thereby obtaining accurate and timely discharge warning of the insulator. The display alarm unit displays the discharge energy level and discharge type according to the output of the signal acquisition and calculation unit, and flashes the light and emits the buzzer alarm when necessary.

[0074] In summary, the present application is composed of a light quantum detection device and a colorimetric analysis method. The light quantum detection device adopts light quantum devices to form four light quantum detection units with independent spectral detection channels. The colorimetric analysis method reflects the discharge state by using the gray scale and chromaticity information of the spectrum, and calculates the B component 0 gray level frequency (P value) in the region by weighting the three color channel brightness values with the total gray value L. The P value curve reflects the discharge light energy distribution, and the present application provides P value criteria for determining initial discharge, medium-high energy discharge and strong discharge. By using the P value curve, the discharge energy can be more accurately tracked and identified, and it is not affected by the background color and material.

[0075] In the second aspect of the present application, an insulator discharge detection system based on a ternary color space is provided. The system is realized by using the method of the first aspect of the present application. The system comprises a collection module, an analysis module, a correlation module and a detection module. The collection module is used to collect abnormal discharge of the insulator by using a four-channel light quantum detection device to obtain four-channel spectral data. The analysis module is used to extract RGB spectral features from the RGB band spectral data in the four-channel spectral data, and perform principal component analysis on the full-band spectral data in the four-channel spectral data based on the RGB spectral features to obtain three principal components. The correlation module is used to calculate the correlation between the principal components and the RGB spectral features by using the load of the RGB spectral features in each principal component, thereby obtaining correlation coefficients. The detection module is used to adjust the RGB band spectral data collected by the RGB spectral channel based on the correlation coefficients, to update the RGB band spectral data, and detect the discharge type of the insulator based on the spectral features in the RGB spectral data.

[0076] The third aspect of the present application relates to a photoelectric sensing device, which is composed of four sensing channels; each sensing channel comprises a front light guide, a light filter unit, a photoelectric sensing array, a signal preprocessing unit, a signal acquisition and calculation unit, a communication unit, a power management module and a display alarm unit; the front light guide and the light filter unit receive light path signals; the photoelectric sensing array, the signal preprocessing unit, the signal acquisition and calculation unit, the communication unit, the power management module and the display alarm unit realize discharge detection and alarm in a discharge detection method based on a ternary color space.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application rather than limit the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A method for detecting discharge of an insulator based on a ternary color space, characterized by, The method comprises the following steps: The four-channel optical quantum detection device is used to collect the abnormal discharge of the insulator to obtain four-channel spectral data; Collecting RGB spectral features and full-waveband spectral features under multiple APD sensor signals in multiple continuous time periods , is a sample number, respectively, RGB spectral features, full-waveband spectral features; The RGB spectral features are extracted from the RGB band spectral data in the four-channel spectral data, the spectral intensity values of each APD in the RGB channel are collected by the optical quantum detection device, the spectral intensity values are converted into gray values, the gray values of all APDs are summarized to obtain a gray matrix of any RGB channel, which is used as the RGB spectral feature, and the full-band spectral data in the four-channel spectral data are subjected to principal component analysis based on the RGB spectral feature to obtain three principal components; The correlation between the principal components and the RGB spectral features is calculated by using the load of the RGB spectral feature in each principal component to obtain a correlation coefficient; The RGB band spectral data collected by the RGB spectral channel are adjusted based on the correlation coefficient to update the RGB band spectral data, and the discharge type of the insulator is detected based on the spectral features in the RGB spectral data; The RGB band spectral data collected by the RGB spectral channel are adjusted based on the correlation coefficient to update the RGB band spectral data, and the discharge type of the insulator is detected based on the spectral features in the RGB spectral data; The RGB band spectral data decomposed in the first to third principal components are used as the adjusted RGB band spectral data, and the intensity difference of the RGB band spectral data decomposed in the first to third principal components is used to adjust the RGB band spectral weight of the summary spectrum; The summary spectrum is: and, , , by , and determined from the intensity difference; The discharge type of the insulator is detected based on the spectral features in the RGB spectral data, and the discharge type of the insulator is detected based on the spectral features in the RGB spectral data; Based on strength differences , , The initial discharge type is obtained, and the intensity difference of any discharge type is pre-calculated; under a preset discharge voltage, the spectral matrix is ​​statistically summarized. The probability distribution of the number of light pulses in the grayscale dimension; The current discharge type is determined by using the probability distribution of the preliminary discharge type and the number of light pulses; wherein is the probability distribution of light pulses at discharge voltage , gray level ; is the number of light pulses at discharge voltage , gray level ; Ptotal(V) is the total number of light pulses at discharge voltage V over all gray levels.

2. The insulator discharge detection method based on a ternary color space according to claim 1, characterized in that: The full-band spectral data in the four-channel spectral data are subjected to principal component analysis based on the RGB spectral feature to obtain three principal components, and the principal component analysis comprises: The first to third principal components of the full-band spectral features are obtained by using a principal component analysis method, taking the sum of variances of the secondary components as the target of the decomposition result.

3. The insulator discharge detection method based on a ternary color space according to claim 2, characterized in that: The discharge type of the insulator is detected based on the spectral features in the RGB spectral data, and the discharge type of the insulator is detected based on the spectral features in the RGB spectral data; Acquisition channel matrix The number of light pulses with a lower gray level of 0 is counted to calculate the pre-breakdown value. The breakdown time of the current discharge is determined based on the pre-breakdown value.

4. An insulator discharge detection system based on a ternary color space, characterized in that: The system is implemented by using the method according to any one of claims 1-3; The system comprises a collection module, an analysis module, a correlation module and a detection module, wherein The collection module is configured to collect the abnormal discharge of the insulator by using a four-channel optical quantum detection device to obtain four-channel spectral data; The analysis module is configured to extract RGB spectral features from the RGB band spectral data in the four-channel spectral data, and to perform principal component analysis on the full-band spectral data in the four-channel spectral data based on the RGB spectral features to obtain three principal components; The correlation module is configured to calculate the correlation between the principal components and the RGB spectral features by using the load of the RGB spectral feature in each principal component to obtain a correlation coefficient; The discharge type of the insulator is detected based on the spectral features in the RGB spectral data. The detection module is configured to implement adjustment on the RGB band spectrum data collected by the RGB spectrum channel through the correlation coefficient, update the RGB band spectrum data, and detect the discharge type of the insulator based on the spectral features in the RGB spectrum data.

5. An optoelectronic sensing device, characterized in that: The device is composed of four sensing channels; Each sensing channel comprises a front-end light guide, a light filtering unit, an optoelectronic sensing array, a signal preprocessing unit, a signal acquisition and calculation unit, a communication unit, a power management module, and a display and alarm unit; The front-end light guide and the light filtering unit receive light path signals; The optoelectronic sensing array, the signal preprocessing unit, the signal acquisition and calculation unit, the communication unit, the power management module, and the display and alarm unit implement discharge detection and alarm in a discharge detection method based on a ternary color space according to the method in any one of claims 1-3.

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