1194results about "Transformers testing" patented technology

The invention relates to the technical field of magnetic bias monitoring, in particular to a real-time monitoring method and system for direct-current magnetic bias of a transformer. The method comprises the following steps: acquiring a primary side current signal and an iron core vibration signal of the transformer; analyzing the zero-flux closed-loop characteristic of the primary side current signal of the transformer and performing temperature drift elimination on the primary side current signal of the transformer to generate a primary side optimization signal of the transformer; performing magnetostrictive vibration noise separation on the iron core vibration signal to generate an iron core vibration separation signal; dynamically filtering a power frequency fundamental wave of the primary side optimization signal of the transformer to extract a pure direct current component; and performing wavelet packet decomposition on the iron core vibration separation signal, and performing magnetostriction characteristic spectrum extraction on the decomposed iron core vibration separation signal to obtain an iron core magnetostriction characteristic spectrum. According to the invention, through multi-source signal fusion and material characteristic modeling, the accuracy, real-time performance and graded protection response capability of transformer DC magnetic bias monitoring are improved.

The invention discloses a transformer fault detection device based on a fuzzy logic algorithm, and the device comprises a data collection module which obtains the operation original data of a transformer in real time through combining the dissolved gas in oil with the temperature, vibration, current and voltage; the data preprocessing module is used for carrying out missing value processing, noise removal and abnormal value detection and processing on the original data; the feature extraction module is used for realizing dynamic feature selection based on data analysis provided by the data preprocessing module; the fault identification module is used for carrying out abnormal waveform judgment on current, voltage, temperature and vibration parameters through a threshold calculation unit and carrying out threshold adjustment based on an optimization algorithm; and the fault detection module triggers the alarm unit or maintains a normal working state according to an identification result of the fault identification module. According to the invention, through monitoring analysis and timely alarm notification, accurate and efficient monitoring of the transformer fault is realized, and stable operation and long-term reliability of the transformer are ensured.

The invention relates to the technical field of transformers, and provides a transformer load capacity prediction method and device, equipment and a storage medium, and the method comprises the steps: obtaining current data, winding temperature data, oil temperature data, environment temperature data and historical load records of a target transformer; calculating a temperature rise correction value based on the winding temperature data and the oil temperature data; after the temperature rise change rate in unit time is calculated through the current data, the environment temperature data and the temperature rise correction value, time sequence matching analysis is carried out on the temperature rise change rate and historical load records, after the load capacity change trend is obtained, comparative analysis is carried out on the load capacity change trend and the historical load records, and a load capacity prediction result is generated. By utilizing collaborative analysis and heat conduction characteristic calculation of multi-source data and comprehensively constructing a time delay interval and a load capacity evaluation method, the precision and applicability of transformer load capacity prediction are improved, and the problem that the prediction precision is insufficient due to the fact that operation data and environment data of the transformer cannot be fully fused during multi-source data processing is solved.

The invention discloses a power transformer arc discharge multi-parameter detection simulation platform and a fault diagnosis method, and relates to the technical field of power system equipment state monitoring and fault diagnosis. The platform comprises a transformer body, a replaceable discharge module, a multi-parameter sensing unit and a signal processing and diagnosis module, and can truly reproduce various typical arc discharge faults of a needle plate, an air gap, a creeping surface, turn-to-turn and the like. The sensing unit is integrated with ultrahigh frequency and ultrasonic sensing probes, high-frequency current and voltage sensors, optical fiber temperature / pressure / strain sensors and the like, so that synchronous acquisition of multi-physical field signals is realized. According to the diagnosis method, through wavelet denoising and multi-dimensional feature extraction, a feature vector of multi-state parameter fusion in the process from partial discharge to arcing is constructed, and accurate classification of fault types is realized by using a support vector machine (SVM) model. The diagnosis method has high accuracy and early warning capability, effectively overcomes the limitation of single parameter diagnosis, and provides reliable technical support for transformer fault research and intelligent operation and maintenance.

Provided are a method for training a transformer fault detection model, a fault diagnosis method, and a related device. The method includes: obtaining an initial voiceprint signal of a transformer and a fault type corresponding to the initial voiceprint signal; preprocessing the initial voiceprint signal to obtain an input signal, and establishing an input signal dataset; performing feature extraction on a first input signal in the training dataset based on a preset feature extraction algorithm to obtain a first voiceprint feature; training an initial detection model based on the first voiceprint feature and a first fault type corresponding to the first input signal to obtain a first training result; determining a loss function based on the first training result and the first fault type; and iteratively adjusting a weight value of the initial detection model until the loss function converges to obtain a fault detection model.

The invention discloses a power transformer winding insulation state monitoring method, a power transformer winding insulation state monitoring device, power transformer winding insulation state monitoring equipment and a medium, and relates to the technical field of power system equipment state monitoring. And an improved particle swarm optimization algorithm is adopted to inversely calculate the insulation parameters of a plurality of windings of the transformer to the ground, so that the insulation state of each winding can be accurately evaluated. Compared with a traditional monitoring method, the method has the advantages that rich information contained in the common-mode leakage current can be fully utilized, the insulation degradation conditions of different windings can be accurately distinguished, more accurate insulation state evaluation is provided, and the safe operation level of the transformer is effectively improved. Moreover, multi-stage evaluation is carried out on the insulation state through a fuzzy comprehensive evaluation method, the insulation state can be comprehensively reflected, different severity degrees of the insulation state can be distinguished, the current state can be evaluated, the degradation trend can be predicted through dynamic indexes, and a scientific basis is provided for operation and maintenance decision making.

The invention relates to the technical field of voiceprint fault diagnosis, in particular to a multi-mode voiceprint fault diagnosis method for a converter transformer. The system comprises a multi-modal signal acquisition module, a multi-modal signal preprocessing module, a feature weighted fusion module, a depth feature extraction module and a fault identification and classification output module. A mechanical vibration signal and a voiceprint feature signal are synchronously collected through a vibration sensor and a voiceprint sensor, and a multi-modal feature vector is constructed after preprocessing; dynamic weighted fusion of vibration and voiceprint features is realized by adopting a channel attention mechanism, and a channel weight is generated through global average pooling and nonlinear mapping; and finally, voiceprint embedding vectors with time sequence distribution characteristics are extracted through attention statistical pooling, and accurate recognition of fault types is realized by adopting a Softmax classifier. According to the method, through collaborative optimization of physical signal coupling, algorithm feature fusion and deep representation learning, the detection capability of the early weak fault of the converter transformer is effectively improved.

The invention relates to the related technical field of distribution transformer insulation aging, in particular to a frequency domain diagnosis method, system and device for distribution transformer insulation aging and a medium, and the method comprises the steps: carrying out the frequency response test of a to-be-tested transformer, and obtaining a first frequency response curve; performing a frequency response simulation test on a transformer equivalent circuit model pre-constructed based on the to-be-tested transformer to obtain a second frequency response curve; constructing a target function based on the deviation of the first frequency response curve and the second frequency response curve; taking element parameters in the equivalent circuit model of the transformer as position vectors, taking the objective function as a fitness function, and adopting a multi-objective particle swarm algorithm to solve the element parameters to obtain optimal values of the element parameters; respectively comparing the optimal values of the element parameters with corresponding normal values to obtain insulation aging degrees; according to the method, the insulation aging degree can be obtained by comparing the element parameters obtained through optimization with the normal unaged element parameters.

The invention relates to the technical field of transformer detection, and discloses a nondestructive testing optimization method and system for an oil-immersed power transformer, and the method comprises the steps: building a three-dimensional dielectric response coordinate system, and generating a preliminary defect positioning map; aging-dominated and damp-dominated defects are detected and identified through spiral frequency sweep excitation; establishing a temperature gradient excitation scheme based on the defect type to generate a defect degree quantitative evaluation index; designing a sound wave modulation excitation scheme to generate a high-resolution defect characteristic spectrum; constructing a multi-mode intelligent sensor network, and combining a defect development trend prediction model to realize defect evolution prediction and generate a graded early warning signal; according to the method, the whole-process accurate detection of the insulation defect of the transformer from positioning, classification and quantitative evaluation to evolution prediction is realized, and a reliable basis is provided for operation and maintenance.

The invention relates to the technical field of capacitance compensation matching, in particular to a dynamic tuning capacitance compensation matching method and system for a transformer simulation test. The method comprises the following steps: before a test is started, establishing a digital twin model based on parameters of a transformer to be tested, identifying a systematic measurement error source caused by inherent characteristics of a system through simulation, and generating error source correction parameters; in the test period, voltage and current original waveforms containing electrical disturbance in a test loop are obtained in real time through high-frequency synchronous sampling; a mixed disturbance separation model is used, the collected original waveform and the generated error source correction parameters are used as input, and random measurement disturbance components caused by load dynamic changes are calculated and separated in real time; and based on the separated disturbance component, driving a dynamic capacitance compensation module to generate a compensatory electric signal which is equal to the disturbance component in size and opposite to the disturbance component in phase, and injecting the compensatory electric signal into a test loop so as to realize active disturbance cancellation. According to the invention, the measurement precision of the transformer simulation test can be improved.

The invention discloses a transformer winding monitoring and diagnosis data acquisition method, system and device and a medium, and belongs to the technical field of power equipment state monitoring, and the method comprises the steps: constructing a multi-scale wavelet transform and improved adaptive Kalman filtering fusion architecture, the multi-scale wavelet transform and improved adaptive Kalman filtering fusion architecture is used for acquiring a monitoring signal of a transformer winding; performing frequency division processing on the monitoring signal by using the multi-scale wavelet transform, and determining each scale coefficient; adjusting a noise covariance matrix based on improved adaptive Kalman filtering, and carrying out filtering processing on each scale coefficient to determine a filtering result; and reconstructing the filtering result into a reconstructed signal, and performing signal quality evaluation on the reconstructed signal to determine the signal quality of the transformer winding. Through multi-scale decomposition and adaptive filtering, the limitation of a traditional method under non-stationary signal processing and strong noise interference is solved, and the retention degree and the signal-to-noise ratio of signal features are improved.

The invention belongs to the technical field of power equipment maintenance, and particularly relates to a transformer winding fault diagnosis method, a terminal and a storage medium, and the method comprises the steps: obtaining frequency response curves of a transformer winding in a normal state and an abnormal state through a frequency sweep test, the head end of the primary side winding is connected with a sweep frequency signal and the tail end is suspended; determining the number of units of an equivalent network according to the number of the frequency response curve characteristic frequencies of the windings in the normal state, and reconstructing a normal winding equivalent network composed of a plurality of RLC units; according to the method, the high-precision equivalent network is constructed, the frequency response residual error analysis is performed, and the Pearson's correlation coefficient is combined to screen the high-correlation element, so that the specific position of the winding deformation or fault can be accurately positioned, the fault type is distinguished, and the limitation that the traditional frequency response method can only judge the overall deformation degree is thoroughly solved.

The invention relates to the technical field of transformers, in particular to a transformer component fault diagnosis method based on machine learning, which comprises the following steps: analyzing medium data acquired by a transformer component, comparing a dielectric loss factor sequence with offset characteristics, training and generating an offset time sequence characteristic set, screening abnormal time periods and aggregating key discharge characteristics, and performing fault diagnosis. And obtaining an abnormal clustering signal group, concluding a trend evolution sequence, performing layered optimization on a path structure, and outputting fault link positioning data. According to the method, various parameters such as dielectric loss, discharge, temperature rise, gas and vibration are integrated, abnormal distribution is actively screened, key node trend change is captured in real time, historical and real-time characteristics in different operation scenes are fused, and efficient analysis of complex link evolution and implicit anomalies is realized. And the fault evolution relationship among the nodes is output in a structured manner, direct application of a diagnosis result in full-link traceability and multi-node trend research and judgment is supported, and the fault diagnosis pertinence and the hidden danger recognition capability are effectively improved.

The invention discloses a transformer fault ultrasonic detection system, which comprises an ultrasonic sensor module, a signal conditioning module, a data acquisition and processing module, a data fusion module, a learning module, a diagnosis decision module and a human-computer interaction interface, according to the ultrasonic sensor module, 6-8 ultrasonic sensors are arranged in a transformer shell in an array mode, piezoelectric ceramic sensors with the resonant frequency of 40 kHZ are adopted as the ultrasonic sensors, the sensitivity of the ultrasonic sensors is-65 dBV / uBar, and effective capture is weak. According to the ultrasonic detection system for the transformer fault, single ultrasonic detection is easily influenced by accidental interference, the system is synchronously connected into equipment such as an infrared thermal imager and a vibration accelerometer, and a multi-physical-quantity correlation analysis model is established. When high-frequency ultrasonic pulses are detected, the system automatically calls temperature data of the corresponding position, and if the temperature gradient exceeds 2 DEG C / cm, it is confirmed that discharging heating is conducted; and if the temperature rise does not exist and the low-frequency vibration of 10-200Hz is accompanied, the mechanical looseness is judged.

The invention provides a transformer winding state detection method based on temperature characteristics. The method comprises the following steps: (1) collecting operation data; (2) preprocessing the operation data; (3) taking the normalized transformer load current, the normalized active power, the normalized top oil temperature, the normalized ambient temperature and the normalized oil flow speed as input data, taking the normalized winding hot-spot temperature as output data, and constructing a data set; (4) establishing a VSN-TKAN-GRN deep learning network parallel computing model of the transformer winding hot-spot temperature considering the multi-head attention mechanism; (5) predicting the hot-spot temperature of the transformer winding based on a VSN-TKAN-GRN deep learning network parallel computing model; and (6) calculating the average relative error percentage of the transformer winding hot-spot temperature prediction result and the actual measurement result, and judging the winding state according to the change of the average relative error percentage. According to the method, the state of the transformer winding in operation can be efficiently and accurately judged, so that effective transformer operation and maintenance measures can be taken in time, and large faults are avoided.

The invention relates to the technical field of transformer safety monitoring, and discloses a transformer explosion-proof intelligent monitoring and early warning system and device. The system comprises a monitoring main control unit, an environment sensing assembly, a data transmission module, a risk judgment module and an interaction warning unit. Before monitoring is started, the environment sensing assembly collects initial parameters through an oil temperature sensor, a pressure sensor and the like, the operation state and the signal transmission environment of the transformer are detected and evaluated, and a state signal is generated and transmitted to the interaction warning unit. And when the state is valid, the data transmission module activates the special link to realize data interaction, monitors data transmission reliability and generates a transmission signal. And when the transmission is qualified, the risk discrimination module deeply analyzes the operation data and generates a discrimination signal. The interaction warning unit triggers an early warning prompt when receiving the state failure signal, transmitting an abnormal signal or judging the abnormal signal. The system can comprehensively monitor the operation of the transformer, guarantee data transmission, accurately discriminate the risk and give an early warning in time, and effectively reduce the explosion risk of the transformer.

The invention discloses a column type distribution transformer winding abnormity detection system and detection method. The detection system comprises a frequency response detection instrument, a signal coupling assembly, an impedance matching module and a data processing module. The frequency response detection instrument is used for outputting a frequency sweep excitation signal and collecting a winding response signal; the signal coupling assembly is used for isolating power frequency voltage and filtering low-frequency interference; the impedance matching module is used for adjusting output impedance and input impedance to realize matching with electrical characteristics of different windings; and the data processing module is used for constructing an equivalent detection loop model, completing frequency response simulation calculation and carrying out frequency domain analysis and anomaly identification on an actually measured response curve. According to the invention, electrified state diagnosis of distribution transformer winding structure abnormity (such as deformation and loosening) can be realized.

The invention discloses a method for predicting the service life of a transformer in a high-proportion new energy access zone based on multi-physics field modeling, and the method comprises the steps: building an electromagnetic field-temperature field coupling simulation model based on a COMSOL simulation platform, and setting the electrical parameters, oil physical parameters and material physical attributes of the transformer; secondly, simulating a photovoltaic access working condition by utilizing a multi-power supply superposition mode, obtaining key data such as three-phase voltage, three-phase current, iron core magnetic flux density, oil temperature and winding temperature, calculating a life label based on a working state, and performing quantitative evaluation on the state of the transformer by adopting a health state scoring method; and finally, constructing a Perii-midFormer life prediction model to realize prediction of the residual life of the transformer. Compared with a traditional life evaluation method, the method fully considers the multi-physical field dynamic coupling effect caused by photovoltaic access, improves the adaptability and reliability of life prediction, and provides an important reference for the operation and maintenance of a transformer in a power distribution network.

The invention discloses a transformer inrush current identification and early warning integrated method based on multi-sensor fusion, and relates to the technical field of transformer equipment, and the method comprises the following steps: S1, carrying out the distributed deployment of multiple sensors and the construction of a self-adaptive sensing network; s2, performing multi-dimensional feature correction and time alignment; s3, fuzzy characteristics are extracted, and a complex nonlinear relation in the operation state of the transformer is captured; s4, fusing the data after the fuzzy characteristics are extracted; s5, constructing an inrush current identification model, and carrying out the precise identification of the inrush current of the transformer; and S6, carrying out real-time early warning. According to the transformer inrush current identification and early warning integrated method based on multi-sensor fusion, dynamic extraction and deep learning modeling of multi-modal features are realized by introducing a distributed multi-sensor fusion technology, and real-time dynamic threshold calculation, reinforcement learning optimization and Bayesian fault probability inference are combined, so that the transformer inrush current identification and early warning integrated method is realized. And integration of inrush current identification and early warning of the transformer is realized.

The invention relates to the field of state monitoring and fault diagnosis of power equipment, in particular to a transformer fault diagnosis method based on a chaos evolutionary optimization algorithm, which comprises the following steps of: 1, acquiring a magnetic flux leakage signal during operation of a transformer; 2, optimizing a parameter modal number K and a penalty factor alpha of variational modal decomposition by using a chaos evolutionary optimization algorithm; 3, performing variational mode decomposition on the magnetic flux leakage signal to obtain an intrinsic mode function component; 4, calculating the envelope entropy of the intrinsic mode function component, and obtaining an effective intrinsic mode function component through screening; 5, extracting the energy entropy and the sample entropy of the effective intrinsic mode function component to form a feature vector; and 6, inputting the feature vector into a pre-trained support vector machine classifier, and outputting a fault type diagnosis result of the transformer. According to the method, the CEO algorithm is combined with the ergodicity of chaotic mapping and the global search capability of the evolutionary algorithm, and the problems that VMD parameters K and alpha are sensitive and depend on experience, and a traditional optimization algorithm is prone to local optimum are effectively solved.

The invention belongs to the technical field of power equipment monitoring, and particularly relates to a transformer load remote monitoring and detecting system. Comprising a data acquisition module, an edge calculation module, a load adjustment module, a load prediction module and a load analysis module. The data acquisition module acquires transformer secondary side load power, current, vibration signals and winding temperature data; the edge calculation module processes load early warning including data denoising and the like; the load adjusting module calculates and adjusts the primary side load power; the load prediction module constructs a model to predict future primary side load power; the load analysis module analyzes the future load condition according to the prediction result. The system realizes remote real-time monitoring, improves data processing precision, load adjustment accuracy and prediction capability, and ensures safe and efficient operation of the transformer.

The invention discloses a high-precision transformer fault detection method and system, relates to the technical field of transformer detection, is used for solving the problems of insensitive fault characterization and delayed diagnosis under a light load condition, and judges whether to enter a load judgment mechanism or not by acquiring input side access voltage information and counting low-load accumulated duration. When entering a load discrimination mechanism, detecting the start-stop behavior of a load branch and the temperature rise trend of a winding, calculating a light load index, identifying a light load state, carrying out region division on an iron core, collecting magnetic flux density data, and calculating iron loss balance characteristics aiming at iron loss deviation which is easy to occur under a light load condition; the insulation degradation coefficient is calculated by fusing the oil temperature peak value and the dielectric loss factor, and the abnormal index is generated with the iron loss balance characteristic, so that the early warning of the early hidden fault of the transformer can be realized in a light load scene in which the traditional temperature rise and current indexes are insensitive, and the operation reliability and maintenance efficiency of the transformer in a power distribution system are improved.

Devices methods for triggering alarms and cautions for electrical equipment may include receiving, at edge device, data of an electrical device, including dissolved gas data or electrical data; setting, based on a comparison of a measurement value of the data to an upper rolling window-based threshold or to a lower rolling window-based threshold, a measurement flag for the electrical device; determining a rate-of-change (RoC) of the data; setting a RoC flag for the electrical device based on a comparison of the RoC to a delta RoC-based threshold; determining an acceleration of the data; setting an acceleration flag for the electrical device based on a comparison of the acceleration to a percentile log-ratio change of measurements threshold; and setting one of a no flag, a caution flag or an alarm flag for the electrical device based on the measurement flag, the ROC flag, and the acceleration flag.

The embodiment of the invention discloses an insulation aging early warning method and device for a medium-high frequency transformer and a medium, and relates to the technical field of transformers, and the method comprises the steps: collecting a multiband light intensity distribution signal of a target resin region in the transformer according to a preset collection frequency through a preset multispectral distributed optical fiber sensing array; the insulation state of the target resin area is predicted through a pre-constructed neural network identification model and a multi-spectral distributed optical fiber sensing array, and the predicted insulation state health degree of the target resin area is determined; acquiring real-time environment parameters of the target resin area, dynamically correcting a preset early-warning discharge threshold according to the real-time environment parameters, and determining a current early-warning dynamic discharge threshold; and extracting a plurality of current light intensity parameters in the multiband light intensity distribution signal, predicting an insulation state health degree and a current early warning dynamic discharge threshold through the plurality of current light intensity parameters, and performing insulation defect early warning on the insulation state of the target resin area.

The invention provides an on-line monitoring system and method for the grounding current of a transformer iron core, and belongs to the field of data processing, and the system comprises a magnetic adsorption type dual-channel current sensor which is used for synchronously collecting a transformer iron core grounding current channel signal and a transformer clamp grounding current channel signal; the self-adaptive signal preprocessing module is used for carrying out self-adaptive differential processing through a self-adaptive differential coefficient and carrying out variable gain adjustment and self-adaptive filtering; the multi-dimensional feature extraction module is used for performing multi-dimensional signal feature extraction on the purified grounding current signal to obtain a multi-dimensional feature parameter set; the feature fusion module is used for performing constraint fusion on the multi-dimensional feature parameter set based on the corrected iron core eddy current loss physical model, and generating a comprehensive health evaluation index through an adaptive weight adjustment mechanism; and the intelligent diagnosis module is used for carrying out transformer iron core fault type identification, health state evaluation and trend prediction according to the comprehensive health evaluation index and the multi-dimensional characteristic parameter set to obtain a transformer iron core state monitoring result.

The invention discloses a transformer health state determination method, system and device and a storage medium, and belongs to the technical field of transformers, and the method comprises the steps: carrying out the temperature detection of a key part of a transformer through a temperature sensor, and obtaining a temperature parameter set; performing stress measurement on the stress concentration area of the transformer to obtain a stress parameter set; inputting the temperature parameter set into an insulation thermal aging model to obtain a thermal aging damage index; performing damage evaluation on the stress parameter set through a Miner rule to obtain a mechanical fatigue damage index; and determining a health state value of the transformer based on the thermal aging damage index and the mechanical fatigue damage index. According to the method, the influence of the temperature on the insulation life is quantified based on the insulation thermal aging model constructed based on the Arrhenius equation, and the mechanical fatigue damage index is accumulated in combination with the Miner rule, so that the health state value of the transformer can be accurately estimated, the accuracy of transformer health state evaluation is improved, and the accident risk of a power system is reduced.

The embodiment of the invention discloses a waveform identification and correction method and system for winding deformation diagnosis, and relates to the technical field of transformer winding monitoring, and the method comprises the steps: synchronously collecting voltage and current signals, and carrying out the preprocessing; performing waveform disturbance type identification on the preprocessed signal, and dividing the disturbance type into harmonic dominant disturbance, voltage sag disturbance or compound disturbance, and harmonic dominant disturbance, voltage sag disturbance or compound disturbance; dynamically selecting a corresponding waveform processing path according to a waveform disturbance type identification result; processing the signal by adopting the selected waveform processing path so as to correct and reconstruct the signal and obtain compensated voltage and current waveform data; by constructing a disturbance identification and path adaptive processing mechanism, the problems of processing lag and compensation distortion of a general algorithm during dynamic signal disturbance are solved, the identification precision is improved, and real-time error correction is realized.

The invention discloses a high-frequency transformer transmission extreme value frequency rapid calibration and tolerance analysis method, which comprises the following steps of collecting operation parameters of a high-frequency transformer in real time, performing standardized preprocessing, and integrating the preprocessed parameters to construct operation condition vectors in a unified format; defining a frequency response range of the high-frequency transformer, constructing a frequency search space based on the frequency response range, and establishing a target function for fitness evaluation based on the frequency search space; searching extreme value frequency points in the frequency search space by adopting a quantum evolutionary algorithm; based on the working condition vector and the extreme value frequency point, a working condition frequency deviation prediction model is constructed, and the working condition frequency deviation prediction model outputs an extreme value frequency shift offset; and calculating the dynamic tolerance bandwidth according to the predicted offset and the fluctuation range of the working condition vector. According to the invention, rapid and accurate calibration of the extreme value frequency of the high-frequency transformer and dynamic determination of the tolerance range can be realized.

The invention discloses a fault prediction method and system for a transformer, and belongs to the technical field of transformer state monitoring and fault prediction. The technical problems that an existing transformer fault prediction method is low in accuracy due to single data modality, insufficient in model generalization ability due to scarcity of real fault data, lack of an adaptive mechanism and the like are solved. According to the technical principle, gas concentration, vibration spectrum, temperature, sound and thermal imaging data are collected through a multi-mode sensor array deployed on a transformer site; a multi-head attention mechanism is adopted in the edge calculation unit to realize multi-source data fusion, and fault prediction is carried out through an LSTM-Attention hybrid model; multi-node model parameters are aggregated through federal learning at the cloud, and model adaptive optimization is realized in combination with reinforcement learning. According to the system, the prediction accuracy is improved, the delay is reduced, training data is expanded by five times through the generative adversarial network, distributed deployment and privacy protection are supported, and an efficient transformer state early warning solution is provided for an intelligent power grid.

The invention discloses a transformer fault diagnosis method and system based on a random forest algorithm, and the method comprises the steps: collecting transformer historical fault data, preprocessing the collected transformer historical fault data, and constructing historical sample data; the method comprises the following steps: collecting real-time operation data of a transformer and preprocessing the real-time operation data to obtain a multi-dimensional feature vector; constructing a training sample set by using the historical sample data, and training a random forest model containing T decision trees by using the training sample set; and inputting the multi-dimensional feature vector into the trained random forest model for diagnosis, outputting a fault diagnosis type, dynamically optimizing the random forest model according to the change condition of the real-time operation data, adjusting the branch decision tree, and restarting branch decision tree training. According to the method, high-precision fault diagnosis and quick response can be realized, the decision reliability is greatly improved, the maintenance cost is reduced, the feature data dimension is reduced, and the sensitivity of fault detection and analysis is improved.