2873 results about "Transformer oil" patented technology

The invention discloses an on-line detection method for gas in transformer oil based on photoacoustic spectrum self-correction, and relates to the technical field of spectrum detection and power equipment state monitoring, and the method comprises the following steps: S1, emitting modulation laser with a specific wavelength to a gas sample released by the transformer oil through a laser, and exciting gas molecules to generate photoacoustic signals; and S2, detecting the photoacoustic signal by using a photoacoustic sensor, converting the signal into an electric signal, and outputting the electric signal. According to the online detection method for the gas in the transformer oil, the correction coefficient is adjusted in real time through a self-correction algorithm, errors caused by light path changes and sensor aging are effectively compensated, the precision and stability of long-term detection are remarkably improved, and the detection accuracy is improved by combining a gas spectrum database and a mode recognition algorithm. According to the method, accurate qualitative and quantitative analysis of various gases is realized, the problem of cross sensitivity of multiple components is solved, and the method also has temperature and pressure compensation functions and can automatically adapt to environmental changes.

The invention relates to the technical field of charging pile group load intelligent regulation and control, and particularly discloses a charging pile group load intelligent regulation and control method and system based on dynamic power equalization, and the method comprises the steps: collecting the oil temperature of a transformer in real time, calculating a total output power threshold value, and correcting the actual output power of a charging pile through combining the line loss and the equipment aging degree; a multi-dimensional load regulation and control decision matrix including user priority, equipment health degree and power regulation sensitivity is established, a dynamic power strategy is allocated through matrix weight, power allocation is inclined to high-priority users and health equipment, emergency power allocation and progressive readjustment during newly-added demands are supported, and the power regulation and control efficiency is improved. Impact of sudden power change on a power grid is avoided, system stability is ensured when newly added loads are accessed, refined intelligent regulation and control of loads of the charging pile group are finally realized, transformer safety, charging efficiency and user experience are improved, and power grid stability and resource optimization configuration are ensured.

The invention relates to a transformer multi-physics field coupling numerical simulation method and device and a storage medium. The method comprises the following steps: performing coupling calculation on a target working condition by using an electromagnetic, heat, flow and force coupling calculation model of a transformer to obtain transformer loss data and temperature rise data; mapping the temperature rise data to a transformer electromagnetic and force coupling calculation model by using a grid node data mapping method to serve as an initial value, and performing coupling calculation on a target working condition to obtain dynamic data of an internal structural member of the transformer; performing fast Fourier transform on the dynamic data in a preset frequency range to obtain dynamic data under each frequency; and mapping the temperature rise data and the dynamic data under each frequency to a transformer sound and force coupling calculation model as initial values, and carrying out coupling calculation on a target working condition to obtain transformer oil tank vibration and external noise data. Compared with the prior art, the method has the advantages that the calculation amount is effectively reduced, and the accuracy of predicting the internal state of the transformer is improved.

The invention relates to the technical field of oil level monitoring, in particular to an oil level state judgment method and system for an oil conservator transformer, and the method comprises the following steps: collecting an oil level signal through a sensor, carrying out the real-time filtering of the collected oil level signal, adjusting a filtering coefficient through the environmental noise level, and recording and outputting the adjusted oil level signal. According to the method, the filter coefficient is adjusted to adapt to environmental noise changes, high adaptability of oil level signal processing is achieved, the system can recognize specific frequency components related to normal operation and potential fault states of equipment through frequency domain analysis, the monitoring meticulous degree and the early warning capacity are greatly improved, and by dynamically monitoring the frequency component changes, the system reliability is improved. According to the transformer oil level monitoring method, abnormity can be detected at the initial stage, equipment faults are effectively prevented, the real-time monitoring capacity is enhanced, the response speed to complex changes is increased, and comprehensive safety guarantee is provided for transformer oil level monitoring.

The invention discloses a high-precision photoacoustic spectrum monitoring method for gas in transformer oil, and relates to the technical field of transformer oil gas monitoring, and the method comprises the following steps: constructing a photoacoustic spectrum detection system, sampling the gas in the transformer oil, and obtaining photoacoustic spectrum data of the gas, the photoacoustic spectrum detection system comprises a near-infrared tunable fiber laser, a photoacoustic cell, a lock-in amplifier and a data acquisition system; preprocessing the acquired photoacoustic spectrum data, and extracting features related to fault diagnosis from the preprocessed photoacoustic spectrum data; and the photoacoustic spectroscopic data of the mixed gas is decoupled by using a chemometrics method. According to the method, the gas photoacoustic spectroscopic data in the transformer oil are collected and analyzed in real time, abnormal conditions in the transformer are found as soon as possible, the mixed gas spectroscopic data are decoupled and separated in combination with chemometrics and a machine learning algorithm, and various gas components are accurately recognized and quantitatively analyzed.

The invention belongs to the technical field of power robots. According to the transformer oil sample collection robot system and method based on the multi-degree-of-freedom mechanical arm, a robot body carries an operation tool integrated board and the multi-degree-of-freedom mechanical arm, and the operation tool integrated board is used for carrying an oil taking gate valve tool and an oil taking butt joint tool; the oil taking tank comprises a rotary door and a rotary door valve connected with the rotary door, a plurality of through holes are formed in the rotary door, and the through holes are used for being opposite to the oil outlet valve and the oil outlet when the rotary door rotates to a set position; the oil taking gate valve tool is used for operating the rotary gate valve and the oil outlet valve to open an oil way under the driving of the multi-degree-of-freedom mechanical arm, and the oil taking butt joint tool is used for being connected with the oil outlet under the driving of the multi-degree-of-freedom mechanical arm; according to the invention, transformer oil sample collection operation can be carried out by virtue of the robot without approaching a person, so that manpower is saved, and the operation quality, the operation efficiency and the operation safety are improved.

The invention provides a transformer partial discharge ultrasonic propagation simulation model construction method and system, and the method comprises the steps: defining a geometric model of a transformer, which is a three-dimensional model of an oil-filled transformer oil tank with a winding and an iron core; setting a sound field module and defining a sound source, and placing the sound source as a partial discharge source in the geometric model of the transformer; a hard boundary condition is applied to the surface of the geometric model tank of the transformer, reflection of the geometric model tank to sound pressure waves is obtained, and under the hard boundary condition, the normal derivative of pressure is zero at the boundary; performing grid division on the geometric model body of the whole transformer by adopting tetrahedral grid units; and after solving, further processing to obtain model parameters, wherein the model is used for displaying the distribution of the ultrasonic sound pressure in the transformer.

A detection method of a power transformer oil leakage detection system based on multi-modal prompt and multi-scale segmentation comprises the steps of data input and preprocessing, feature extraction, multi-modal feature fusion, Transformer decoder decoding, segmentation and classification, model training and fine adjustment and output and result display, and by combining Mask2Former and CLIP models, multi-modal information is fully utilized, and the detection result is more accurate. According to the method, the accuracy and robustness of oil leakage detection are improved, the characteristics of multi-modal information fusion, high-precision target segmentation, high robustness and high expandability are realized, accurate identification and segmentation of an oil leakage area are realized, other transformer fault detection tasks are adapted by simply modifying prompt words, and the method has good universality and adaptability.

The invention discloses a transformer oil conservator oil level online monitoring method and system based on an ultrasonic technology, and belongs to the field of power equipment monitoring. The oil level of the transformer is related to equipment safety, a traditional monitoring means has defects, and false oil level is prone to misleading operation and maintenance. With the ultrasonic technology as the core, the multi-band ultrasonic sensor transmits and receives ultrasonic waves to the oil conservator, obtains oil level signals and collects oil temperature and load data at the same time. The oil level is calculated by means of ultrasonic propagation time and a multi-band weighted average algorithm, and the false oil level is discriminated according to oil temperature compensation and load correlation. Through signal conditioning and acquisition, microprocessor operation and wireless communication transmission, data is stored and analyzed in a background monitoring system. The innovative system architecture and algorithm realize high-precision oil level measurement, strong interference resistance, intelligent false oil level discrimination and real-time remote monitoring. On-site tests show that the system stably and accurately monitors complex working conditions, effectively prevents oil level abnormal faults, improves the operation and maintenance efficiency of the transformer and the power supply reliability of a power grid, and firmly guarantees the reliable operation of a power system.

The invention discloses a fault detonation simulation test method and platform for an extra-high voltage converter transformer network side bushing, and the method comprises the steps: providing a critical value calculation method for primary physical explosion and secondary chemical explosion of the extra-high voltage converter transformer network side bushing under different arc fault energy conditions; and the critical conditions of primary physical explosion and secondary chemical explosion can be accurately judged. According to the invention, the whole dynamic evolution process from rapid cracking of transformer oil and sudden pressure increase caused by converter transformer fault arc to superposition of physical explosion and explosion chemistry can be truly simulated, a grid-side bushing fault detonation critical discrimination method is provided, and technical parameters and theoretical support are provided for the explosion-proof and pressure-relief design of the converter transformer.

The invention provides an intelligent analysis method for the oil temperature and the oil level of a transformer. The method comprises the following steps: S1, acquiring oil temperature and oil level data of a transformer, generating displacement diagrams of two data in a coordinate system, drawing an actual oil temperature and oil level curve, and comparing the actual oil temperature and oil level curve with a standard oil temperature and oil level curve given by a manufacturer; s2, generating a relation function between the oil level and the oil temperature by using the transformer oil temperature and oil level monitoring, checking and analyzing device, and performing data comparison with a standard oil temperature and oil level curve given by a manufacturer; s3, analyzing whether the deviation between the oil temperature and oil level curve and a standard oil temperature and oil level curve given by a manufacturer exceeds an allowable value or not under the high-temperature and low-temperature operation conditions; according to the invention, quantitative data analysis can be carried out on the oil temperature and the oil quantity of the oil-immersed transformer.

The invention discloses an oil temperature monitoring system and method for an oil-immersed transformer, and relates to the technical power equipment state monitoring field, and the system comprises a temperature sensor module, a data acquisition module, a data processing and analysis module, an early warning module and a fault diagnosis module; the temperature sensor module comprises a plurality of temperature sensors distributed at different positions of the oil-immersed transformer and is used for acquiring oil temperature data in the transformer in real time; the data acquisition module is connected with the temperature sensor module and is used for collecting oil temperature data acquired by a temperature sensor and transmitting the data to the data processing and analyzing module; the data processing and analyzing module is used for processing and analyzing the collected oil temperature data; the early warning module is used for sending out an early warning signal when the data processing and analyzing module detects that the oil temperature is abnormal or predicts that the hot spot temperature exceeds a safety threshold value; according to the invention, the problem that the fault of the transformer cannot be accurately monitored and intelligently diagnosed is solved.

The invention discloses an environment-adaptive transformer oil sample Raman spectrum detection method and related equipment, and relates to the field of optical sensing systems. The method comprises the following steps: collecting oil sample Raman spectrums and environmental parameters in multiple operation scenes, and constructing a multi-scene spectrum characteristic model and a standard fingerprint database; pre-processing and denoising parameters are adaptively set based on the environmental perception vector, and baseline correction and joint denoising are carried out on the original spectrum; scene discrimination is carried out by fusing the characteristics of peak position, peak height, peak width, integral area and the like, a scene-related component standard spectrum dictionary is generated, and the concentration and confidence of each target component are obtained by adopting constrained spectral line unmixing and quantitative calibration; and driving the fingerprint database and the model to update in combination with quality control indexes such as spectral shape relevancy and residual errors and a drift detection result. The system is composed of a Raman spectrum acquisition module, an environment monitoring module and a data processing module, and can improve the robustness and quantitative precision of Raman detection of transformer oil in a complex environment.

The invention discloses a mixed gas concentration detection method based on a temperature modulation technology, and relates to the field of semiconductor gas sensing. The problem that the concentration of the mixed gas is difficult to analyze more accurately in the prior art is solved. According to the method, the characteristics of the gas sensitive response data are expanded under the scale of the spatial domain through the temperature modulation technology, the time sequence data are processed by adopting the LSTM neural network model, the characteristic information contained in the sequence data is fully mined, and the recognition precision of the concentration of the mixed gas is remarkably improved through the combination of the temperature modulation technology and the LSTM neural network model. The device and the method are mainly used for detecting the concentration of H2 and CO gas released from the transformer oil in case of failure.

The invention discloses a power transformation equipment bimodal defect identification method and system. The method comprises the following steps: collecting a transformer oil leakage defect visible light image sample and a power transformation equipment heating defect visible light and infrared bimodal image sample; building an improved YOLOv8 instance segmentation model, and expanding a defect sample by adopting an image segmentation and background fusion method; the method comprises the following steps of: constructing a fusion image data set through image registration and fusion, constructing an improved YOLOv8 target detection model, using a transformer oil leakage defect sample and a fusion image sample for training the improved YOLOv8 target detection model, and identifying a transformer oil leakage defect and a power transformation equipment heating defect through the trained improved YOLOv8 target detection model; and presenting a heating defect detection result of the fused image in the visible light image by adopting a mapping method. By introducing a deep learning method into the technical field of operation and maintenance of the power transformation equipment, efficient, accurate and intelligent recognition of structural defects of the power transformation equipment is realized.

The invention discloses a transformer fault detection method based on gas in oil and related equipment, relates to the technical field of power equipment monitoring, and effectively solves the problem that the content difference of characteristic gas of the same fault type is large under different transformer oil temperatures due to the fluctuation of the transformer oil temperature in the traditional method by adopting the technical scheme. The detection system eliminates the influence of the oil temperature change on the gas content through oil temperature correction and standardization processing, so that the fault judgment of the transformer is more accurate and reliable. And meanwhile, the detection system adopts the characteristic gas ratio matrix to judge the fault type, so that misjudgment possibly caused by pure dependence on a content absolute value is avoided, and the accuracy and reliability of transformer fault diagnosis are improved.

The invention discloses a transformer fault diagnosis method based on a time-frequency domain feature interactive fusion network. The method comprises the following steps: data collection: collecting a data set of dissolved characteristic gas in transformer oil with a marked type; data preprocessing: removing obvious error data through data cleaning, and then processing unbalanced data by using oversampling and downsampling; fault type judgment is carried out based on three pairs of ratio coding combinations of gas; network construction: constructing an interactive fusion network based on time-frequency domain features; network training: inputting the training data set into the constructed network for network training; and fault classification: inputting the test data set into the trained time-frequency domain feature-based interactive fusion network to obtain a classification result. The method improves the feature expression capability, reduces the overfitting risk, improves the accuracy and efficiency of fault diagnosis, and can be used in the field of transformer operation and maintenance.

The invention discloses a method and a system for simulating bubble motion in transformer oil by considering multi-physics field coupling, and the method comprises the steps: fusing an energy equation into a mass conservation and momentum conservation equation, and constructing a multi-physics field model which considers the coupling of an electric field, a thermal field and a flow field; introducing a k-epsilon turbulence model into the multi-physics field model to obtain a first optimized multi-physics field model; the k-epsilon turbulence model is an oil flow model in a large transformer; introducing a phase field method into the first optimized multi-physics field model to obtain a second optimized multi-physics field model; various forces of bubble motion are introduced into the second optimized multi-physics field model, and a third optimized multi-physics field model is obtained; and performing parameter solving on the third optimized multi-physical field model to obtain the movement process of the bubbles in the transformer oil. According to the invention, a multi-physical field model considering coupling of an electric field, a thermal field and a flow field is constructed, and comprehensive and accurate simulation of bubble motion in transformer oil is realized.

The invention discloses a transformer oil leakage detection method and device based on polarization-ultraviolet fusion imaging, and the device comprises a housing, a support, a camera, an environment induction device, an overall controller, a power supply, a polaroid rotating wheel assembly, a lens seat assembly, and an image processor. And the motor driver, the illumination intensity sensor, the ultraviolet irradiance sensor, the image processor and the power supply are electrically connected with the integral controller respectively. According to the invention, a multi-angle polarization image and a non-polarization image in the same field of view can be acquired, and then polarization characteristics contained in an oil stain fluorescence signal are extracted and effectively distinguished from non-polarization characteristics of background light; in the daytime mode, the transformer oil leakage detection device can identify a target area with relatively high polarization intensity, and through image fusion enhancement processing, non-target background signals are significantly suppressed, and the fluorescence significance of an oil leakage characteristic area is improved.

Provided are a fault diagnosis apparatus and method based on fluorescence multivariate correction analysis of transformer oil, relating to the field of transformer fault diagnosis technology. Thus, the problem of large volume and weight of the apparatus, high costs, and inconvenience to use caused when in the related art, a fluorescence spectrometer is directly used to acquire the fluorescence spectrum of transformer oil is solved. The monochromatic excitation light of an optimal excitation wavelength generated by a fluorescence excitation source is used to excite the transformer oil in a fluorescence excitation detection apparatus to generate fluorescence. The fluorescence excitation detection apparatus generates the fluorescence according to the input monochromatic excitation light and inputs the fluorescence to a fluorescence signal acquisition and analysis apparatus. Since the fluorescence signal acquisition and analysis apparatus acquires and analyzes the fluorescence signal emitted by the transformer oil by using a multivariate correction filter group, the emission monochromator component of the fluorescence spectrometer is replaced. In this manner, the equipment costs and the equipment volume are reduced, and data processing is rapid. Thus, the cost performance of the fault detection of the transformer oil is improved, thereby implementing the engineering application of the fluorescence monitoring technology in the online diagnosis of the transformer failure.

The invention discloses a transformer oil chromatographic analysis method based on an improved fuzzy clustering algorithm and an LSTM (Long Short Term Memory) algorithm. The method comprises the following steps: acquiring sample data; s2, randomly dividing the sample data into a plurality of cluster sets; s3, obtaining an initial clustering center of the clustering cluster set; s4, acquiring a comprehensive distance between the initial clustering center of each clustering cluster set and the sample data about the Euclidean distance and the cosine distance; s5, the membership matrix is updated according to the comprehensive distance, the steps S3 to S4 are repeatedly and iteratively executed until the iteration convergence condition is met, and the final clustering center of the clustering set is obtained; and according to the final clustering center, determining a clustering result of the transformer fault type. S6, dividing the clustering result into a data training set and a data test set according to a preset proportion; s7, establishing an LSTM model and taking the LSTM model as a prediction model for transformer fault analysis; and obtaining an optimal prediction model based on the data training set and the data verification set. The problems that a large amount of manpower and financial resources are consumed in a traditional regular overhaul mode, particularly in remote areas with inconvenient traffic, overhaul work faces many challenges, and in addition, human errors possibly occur in the overhaul process, and equipment is damaged are solved.

The invention discloses a method and a system for identifying mixed gas in transformer oil, a product and a medium, and relates to the technical field of testing or analyzing materials by means of measuring chemical or physical properties of the materials, and the method comprises the following steps: acquiring an internal structure diagram of a transformer, determining spatial position relations of parts and corresponding temperature distribution characteristics, and obtaining solubility differences of different spatial positions according to the spatial position relations and the corresponding temperature distribution characteristics. During operation, mixed gas concentration data of a plurality of detection points are collected, initial gas concentration data are recorded when an operation threshold range is deviated, and gas concentration change data are corrected according to solubility differences of different temperature zones. Calculating gas concentration space-time distribution characteristics, and determining a gas accumulation area according to the gas concentration space-time distribution characteristics and a space position relation. And separating the mixed gas components by using a gas chromatographic method, comparing preset gas types with the content of each component, and determining the corresponding gas type of each internal region. By implementing the method, the accuracy of identifying the types of the mixed gas in the transformer oil can be improved.

Disclosed in embodiments of the present application are a fault identification method and apparatus for a transformer, a device, and a medium. The method comprises: acquiring concentration data of dissolved gas in transformer oil; and processing the concentration data on the basis of an optimized probabilistic neural network model to obtain a fault type of a transformer, wherein the optimized probabilistic neural network model is determined on the basis of a target smooth factor parameter, the target smooth factor parameter is determined from among candidate smooth factor parameters by means of an artificial jellyfish search algorithm, and the candidate smooth factor parameters are smooth factor parameters meeting a fitness condition.

The invention discloses vacuum degassing and automatic oiling equipment for a transformer, and relates to the technical field of transformer oiling. Comprising an oil injection device, the bottom of the oil injection device is provided with an anti-bubble suction part, and the anti-bubble suction part is used for preventing bubbles; a mistaken suction prevention part is mounted on the bubble prevention suction part; the mistaken suction prevention part is used for preventing transformer oil from being sucked mistakenly; a floating lifting adapter is mounted on the oil injection device; a film spacer is mounted on the floating lifting adapter; the film spacer is used for spacing new oil and old oil; the thin film spacer can be matched with the floating lifting adapter, so that new oil and old oil are isolated during oil change, mixing of the new oil and the old oil can be reduced, oil change quality is improved, and waste of the new oil is reduced; the problems that an existing transformer vacuum degassing automatic oiling device can only conduct small-amount oiling generally when a transformer does not stop working, and new and old oil isolation replacement oiling is not convenient to conduct are solved.

The invention relates to the field of marine transformers, and discloses a marine transformer with a protection mechanism, the marine transformer comprises a transformer oil tank and a mounting seat, the transformer oil tank is arranged above the mounting seat, a movable supporting structure is arranged in the middle of the upper surface of the mounting seat, and the transformer oil tank is arranged on the mounting seat through an external protection structure; a mounting frame is fastened in the middle of the lower inner wall of the transformer oil tank, a winding is mounted on the mounting frame, and oil sliding up and down in the transformer oil tank can be blocked through an internal protection structure when the ship body shakes up and down to drive the oil to slide, so that the upper side face and the lower side face of the transformer oil tank are protected; the problem that the upper side face and the lower side face of the transformer oil tank deform due to impact is reduced, the upper side face and the lower side face of the transformer oil tank can be protected outside the transformer oil tank through the external protection structure and the auxiliary protection structure, and the protection effect is better.

The invention relates to the technical field of transformer oil level detection, and discloses an oil level detection system for a liquid-immersed transformer, and the system comprises a detection module which is disposed in an oil cavity of the transformer, and the detection module is configured to obtain temperature data, oil level data and vibration data in the oil cavity of the transformer; the central control module is electrically connected with the detection module, and the central control module is configured to correct the oil level data according to the temperature data and the vibration data; and the early warning module is electrically connected with the central control module, and the early warning module is configured to determine whether to send early warning information or not according to the corrected oil level data. Intelligent correction and risk judgment can be carried out on the oil level information based on the multi-source sensing data, and the accuracy of transformer oil level monitoring and the reliability of early warning response are remarkably improved.

The invention discloses a transformer diagnosis method and system based on insulating oil detection. The diagnosis method comprises the following steps: acquiring gas concentrations of gas in transformer oil at a plurality of different time nodes within a time range; fitting according to the gas concentration of each component gas under the plurality of time nodes to obtain a gas dissipation curve of each component; calculating the gas production rate of each component gas in the time range according to the gas dissipation curve; obtaining a ratio matrix of the gas production rates of the component gases; and predicting a transformer operation fault corresponding to the ratio matrix through a neural network model. According to the method, the neural network model is utilized to predict the ratio matrix established among the multiple component gases, so that the comprehensive operation state of the transformer corresponding to the ratio matrix is obtained.

The invention discloses a method, device and system for classifying and recognizing impurity particles in transformer oil and a medium, and the method comprises the steps: obtaining an image of the impurity particles in the transformer oil in real time, taking the image as an original image, and forming a data set based on the original image; preprocessing the original image in the data set to obtain a preprocessed image; a K-means + + algorithm is adopted to generate a new prior frame; according to the new prior frame, training a YOLO target detection algorithm to obtain a trained YOLO target detection model; and performing target detection on the preprocessed images based on the trained YOLO target detection model, automatically identifying different types of impurity particles, automatically counting the number of prediction frames on all the images, and outputting the number of different types of particles. According to the invention, the accuracy and efficiency of monitoring the particle impurities in the transformer oil can be effectively improved, and important reference is provided for state evaluation and fault detection of the transformer.

The invention provides a power transformer free gas detection method and system for material screening based on machine learning, and the method comprises the steps: carrying out the sampling of a compound space, and calculating a material characteristic data set of different doping configurations for the adsorption performance of a target gas through a high-throughput first principle; based on the material characteristic data set, a CGCNN convolution kernel is fused through a GNN framework, and a pre-trained material screening model is obtained and used for predicting the adsorption performance of different doping configurations to target gas; screening by utilizing a pre-trained material screening model to obtain an optimal doping configuration meeting a preset performance threshold value; screening based on the optimal doping configuration to obtain an optimal gas sensitive material; acquiring a response signal of the sensor array to the dissolved gas removed from the transformer oil, identifying the type of the target gas, and estimating the corresponding gas concentration; wherein the sensor array is prepared from an optimal gas sensitive material. According to the invention, rapid, accurate and on-line detection of various key free gases in transformer oil can be realized.

The invention discloses a transformer fault diagnosis method, system and equipment and a medium, and the method comprises the steps: through the linkage design of an oil pumping sampling module and a transparent sampling channel, combining high-speed camera shooting collection and synchronous light source illumination, and obtaining the image of particles in a transformer oil sample in real time; secondary pollution and personal errors possibly caused by traditional off-line sampling are avoided, and the detection efficiency and the objectivity of data are improved; the ResNet neural network is introduced to carry out feature extraction on the oil sample image, the particle number and particle size distribution information are automatically identified, the complex process of manually designing features in a traditional image processing method is avoided, and the accuracy and robustness of diagnosis are greatly improved; by setting judgment threshold values for indexes such as the particle number, the average particle size and the large particle size proportion, logic judgment is conducted in combination with the actual operation state, potential abrasion, pollution and other abnormalities can be recognized in the early stage, the capacity of quick response and advanced intervention is achieved, and the operation safety and the intelligent operation and maintenance level of the transformer are remarkably improved.