21346 results about "Transformer" patented technology

The invention discloses an electric energy metering box fault prediction method and system based on big data analysis, relates to the technical field of smart power grids, and solves the problems of progressive aging missing detection and instantaneous interference misjudgment caused by dependence on single parameter threshold alarm and fault positioning misalignment caused by multi-source data isolated analysis in the prior art. According to the scheme, electrical, environment and equipment state parameters are collected in real time through a multi-dimensional sensing network; the error drift of the mutual inductor is dynamically predicted based on an LSTM-Kalman filtering model, and core breakdown early warning is realized in combination with wavelet transform; outputting a corrected resistance value and a fault mark by using a BP neural network; predicting the life of the piezoresistor by adopting a gradient boosting decision tree and fusing lightning overvoltage characteristics; the transient interference is suppressed through the combination of a Transform self-attention mechanism and dynamic time warping; according to the method, the aging detection precision and the complex environment adaptability are remarkably improved, the misjudgment rate is reduced, and the multi-fault associated positioning and active defense capability is realized.

Electric heat for generating smoke from tobacco or the like in a hookah type smoking pipe. Electric heat is obtained from an electrically powered heating element which may be placed proximate the tobacco. The heating element may be contained within a housing which in turn may be placed above the smoking chamber of the hookah. The housing may have adjustably damped holes disposed to pass air over the heating element. Electrical circuitry serving the heating element may comprise a step down transformer and a voltage adjusting switch. The heating element may be integral with the hookah, may take the form of a separate component which is mountable over the smoking chamber of the hookah, or may comprise a free standing assembly which may be placed to stand adjacent to the hookah.

The invention discloses a self-adaptive frequency spectrum monitoring and interference suppression method for a railway power transformer. The method comprises the following steps: S1, collecting original multi-source signal data; s2, performing high-order filtering and Z-score normalization processing on the original multi-source signal; s3, inputting the original multi-source signal into a multi-scale residual fusion time-frequency transformation network, and extracting a time-frequency feature tensor; s4, inputting the time-frequency feature tensor into the interference identification network fused with the attention mechanism; s5, dynamically activating an interference suppression module according to an identification result; s6, constructing a multi-dimensional tensor data structure, extracting sparse dictionary morphological features and spectral domain statistics, and generating a composite feature vector set; s7, inputting the composite feature vector into a health state evaluation module; and S8, uploading the diagnosis result to a remote monitoring platform through the embedded communication module. According to the invention, multi-dimensional perception and adaptive modeling are fused, and intelligent identification and remote monitoring of railway transformer faults are realized.

The invention discloses a low-voltage transformer area examination meter practical training device and a fault simulation control method, and relates to the technical field of electric power practical training. The device aims at the problems of single scene, extensive evaluation and low skill improvement efficiency in traditional electric power practical training. The meter simulation module is compatible with various meters and is matched with a dual-communication protocol to realize efficient data transmission; the intelligent interaction module provides high-quality interaction experience through a high-definition touch screen and low-delay transmission; the data generation module can quickly switch customized scenes, and the fault simulation module can accurately inject various faults; the training management module depends on advanced technologies of knowledge maps, deep learning and fuzzy comprehensive evaluation to realize full-process closed-loop management from project design and operation guidance to evaluation feedback and intelligent optimization; all the modules cooperate efficiently, and the professionality, the accuracy and the efficiency of power failure troubleshooting and operation and maintenance skill training of trainees are remarkably improved.

The invention discloses a power transformer partial discharge positioning method based on multi-sensor array fusion, and the method comprises the following steps: S1, selecting a sensor installation point, and laying a multi-sensor array structure; s2, partial discharge signals of the three types of sensors are collected, and primary signal processing is carried out; s3, calculating propagation time differences between the reference channel and other channels by adopting a generalized cross-correlation weighting algorithm, and generating a time difference matrix; s4, constructing a TDOA model in combination with the layout coordinates and the time difference matrix, and solving three-dimensional initial coordinates of a power supply; s5, establishing a structure correction model, compensating the path deviation, and outputting corrected positioning coordinates; s6, calculating an error and generating a confidence score; s7, mapping a positioning result to the three-dimensional model and generating an image; and S8, writing the positioning information into a database for filing management. According to the method, the multi-frequency sensor and the path correction model are fused, and high-precision three-dimensional positioning of partial discharge of the transformer is realized.

The invention relates to the technical field of power equipment state monitoring, in particular to an oil-immersed transformer distributed temperature measurement method based on a fluorescent optical fiber sensor, and the method comprises the steps: arranging the fluorescent optical fiber sensor in a key temperature rise region in a transformer in a partitioned and layered three-dimensional topological structure, and forming a distributed temperature measurement network; exciting light is injected through a pulse laser to excite a fluorescence signal; a dual-channel phase-locked amplification technology is used to collect signals, and a dual-weight adaptive attenuation model and an oil flow coupling compensation function are combined to demodulate the temperature; reconstructing a dynamic temperature field based on a three-dimensional thermodynamic inversion algorithm, and marking high-gradient hot spots; and temperature, load current and oil flow velocity data are fused to realize graded alarm. According to the invention, the limitation of traditional single-point monitoring is broken through, the strong electromagnetic interference resistance is excellent, a global temperature field can be accurately reconstructed, dynamic early warning is realized, and the operation safety and the operation and maintenance efficiency of the transformer are remarkably improved.

The invention discloses a deep learning-combined power transformer partial discharge signal extraction and diagnosis method. The method comprises the following steps of S1, setting a multi-channel synchronous acquisition system in a power transformer body area to acquire a multi-dimensional original partial discharge data set; s2, preprocessing the acquired multi-dimensional original partial discharge data set; s3, performing time alignment and amplitude matching on the processed signal, and dividing the processed signal into a sliding time window to construct a standard input tensor; s4, constructing an attention enhancement model fused by the convolutional neural network and the bidirectional gating circulation unit; s5, performing supervised training on the attention enhancement model by using the labeled sample; s6, inputting the real-time signal into the training model, and outputting a discharge type label; s7, risk grade evaluation is carried out in combination with statistical characteristics; and S8, generating a structured diagnosis report and uploading the structured diagnosis report to a monitoring platform. According to the invention, multi-source signals and a depth model are fused, and intelligent diagnosis and risk assessment of transformer partial discharge are realized.

The invention relates to the technical field of abnormity early warning, in particular to a transformer load abnormity early warning method and system, and the method comprises the following steps: obtaining an electrical parameter sequence and correcting a phase, constructing a current and power change rate sequence, extracting a load fluctuation trend factor, calculating a disturbance growth rate and judging whether the disturbance growth rate crosses a boundary, and recognizing a power gradient abrupt change feature. And judging whether periodic overlapping exists or not, and outputting a combined abnormity early warning identification sequence. According to the invention, by introducing phase difference and time base offset calculation, constructing a phase-aligned electrical parameter sequence group, and analyzing a periodic rate mean value and an integral quantity, disturbance trend evaluation is realized, a power gradient sequence is used to identify a section mutation interference signal, and a joint anomaly identification mechanism is constructed on two dimensions of a period and a section. The method achieves the composite judgment of the load disturbance trend and sudden change interference, effectively improves the accuracy and response time efficiency of load abnormity early warning, and reduces the recognition error risk caused by hidden fluctuation or local sudden change.

The invention relates to the technical field of transformer monitoring, and discloses an explosion-proof intelligent monitoring and early warning device for a transformer. The device comprises a multi-source sensing module used for collecting multi-dimensional heterogeneous data of transformer operation; the feature extraction module is used for fusing data cross-domain features to generate various feature representations; the anomaly detection module is used for generating an abnormal signal space-time incidence matrix based on a dynamic causal network construction model; the risk early warning module outputs a risk level and an early warning instruction through a multi-task decision-making mechanism; and the self-adaptive regulation and control module is used for optimizing monitoring parameters and hardware resource allocation according to instructions. The device also can carry out critical state identification and emergency intervention, and constructs an insulation degradation prediction model to correct an early warning threshold value. According to the device, omnibearing monitoring, accurate early warning and intelligent regulation and control of the transformer are realized, the operation safety and reliability of the transformer are effectively improved, and the fault risk and loss are reduced.

The invention relates to the technical field of industrial component detection, in particular to a transformer iron core detection method based on computer vision, which comprises the following steps of: acquiring an iron core image, extracting key pixel characteristics, screening a directional scattering abnormal region to generate an interference map, extracting a consistent gradient region correction image to generate a reconstruction map, and positioning a symmetric disturbance generation structure map by integral gray difference. And analyzing an overlapping relation by a superposition structure graph to generate an abnormal component graph, and evaluating a risk level by matching a reference index to generate an early warning graph layer. Interference reflection and structural features can be distinguished through linkage analysis of the pixel direction vector and the brightness change frequency, correction of a distorted area in an image is realized based on a gray statistical stable value, and the distortion of the image is corrected by constructing a symmetric point map and analyzing the change trend of a gradient difference value sequence. And the structural overlapping relation is quantitatively judged by combining a component mapping profile diagram, so that the relevance between an abnormal region and a key component is clearly expressed, and the grading evaluation capability of various fault risks in the iron core is improved.

The invention relates to a low-rank fine-tuning transformer fault diagnosis method based on adaptive attention guidance, and belongs to the technical field of artificial intelligence. An attention scoring mechanism, an adaptive attention scoring mechanism, a dynamic rank allocation strategy and a hierarchical learning rate adjustment mechanism are introduced, and a context-aware dynamic updating strategy is further fused, so that the low-rank fine-tuning transformer fault diagnosis method based on adaptive attention guidance is realized. According to updating of real-time performance, loss and gradient dynamic intelligent triggering key parameters in the model training process, a large-model lightweight adaptation frame suitable for a transformer fault diagnosis task is constructed, and on the premise that diagnosis accuracy is ensured, model fine adjustment and deployment cost is remarkably reduced.

The invention discloses a multi-dimensional information integrated transformer health state monitoring method, which relates to the technical field of power systems, and comprises the following steps: deploying a multi-source sensor, collecting various signals of a transformer, and carrying out signal conditioning and analog-to-digital conversion on analog signals collected by the sensor; performing timestamp alignment on the received multi-source heterogeneous data, and eliminating data noise by adopting a filtering algorithm; key features which are sensitive to the health state of the transformer and complement each other are selected from the extracted feature values to form a health state evaluation index set; setting a sliding time window, obtaining historical data in the window, performing normalization processing on the data, calculating the information entropy and the variation coefficient of each index, and fusing the information entropy and the variation coefficient to obtain the dynamic weight of each index; obtaining a health state index of the transformer according to the normalized value and the dynamic weight; and displaying the health state index and the change trend thereof in real time, and setting an early warning threshold value and an alarm threshold value of the health state index.

The invention discloses a multi-parameter measurement system and method for a power frequency non-partial discharge test transformer, and relates to the technical field of state monitoring of high-voltage test equipment and power equipment, and the system comprises a synchronous trigger unit which is connected to a power frequency voltage signal collection end and is used for detecting a zero crossing point of a power frequency voltage signal and generating a synchronous trigger signal, a power frequency period is divided into a plurality of sub-windows with equal phase angles. According to the multi-parameter measurement system and method for the power frequency non-partial discharge test transformer, the problem of phase mismatch of a power frequency signal and a high-frequency partial discharge signal in traditional multi-parameter measurement is effectively solved through a power frequency phase locked synchronous trigger mechanism and a dynamic noise suppression technology; and the positioning precision of the partial discharge source and the insulation defect diagnosis reliability are improved. The nanosecond-level time sequence error control is realized, and the power frequency coupling interference is inhibited while the details of the high-frequency pulse are kept by combining a phase correlation dynamic filtering strategy, so that the signal-to-noise ratio of the weak discharge signal is improved.

The invention relates to the technical field of knowledge graph construction, and discloses a transformer substation knowledge graph construction and optimization method based on multi-view learning, which comprises the following steps of: processing transformer substation multi-source data through a heterogeneous model; multi-source heterogeneous data of operation and maintenance texts, monitoring data, regulations and rules and infrared images of substation equipment are mapped to a unified feature space through linear projection, a multi-mode positive and negative sample pair of the same equipment is constructed, the distance of related equipment features is shortened by adopting comparative learning, projection matrixes of various data are dynamically optimized, and the multi-mode heterogeneous data of the substation equipment is obtained. And jointly detecting the power transformation equipment entity boundary in the operation and maintenance text and the equipment monitoring data, fusing the multi-modal equipment characteristics through an attention mechanism, and reasoning the relationship type between the equipment. According to the method, the multi-source heterogeneous data of the transformer substation and expert experience are deeply fused, so that the fragmentation and staticization problems of a traditional knowledge management system are effectively solved, and the accuracy of state perception and fault diagnosis of the power equipment is remarkably improved.

The invention is suitable for the field of transformer fault analysis, and provides a transformer potential fault mode identification method and device based on reverse derivation, and the method comprises the steps: generating a multi-field coupled feature puzzle model through real-time collection of four-dimensional physical quantity spatio-temporal data of temperature, vibration, oil chromatography, partial discharge and the like, and carrying out the topological matching of the feature puzzle model and a historical health file, and reversely deducing a fault coupling path for the abnormal vacancy region through a constraint satisfaction algorithm, reconstructing a missing feature block, decoupling and extracting interface abnormal features, dynamically optimizing model topology in combination with an equipment aging factor, and finally quantifying the full-life-cycle risk accumulation intensity and positioning a composite fault source. The method breaks through the limitation of a traditional single-parameter threshold value, achieves the early recognition and precise traceability of the multi-field coupling fault through a reverse derivation mechanism of the feature jigsaw blocks, is suitable for the health management of the whole life cycle of the transformer, and can remarkably improve the timeliness and accuracy of potential fault early warning.

The invention discloses a system for testing an electromagnetic shielding coupler of a testing transformer based on intelligent sensing, and relates to the technical field of electrical equipment testing, and the system comprises a flexible distributed intelligent sensing array which is composed of a plurality of high-density miniature electromagnetic sensors and a temperature-vibration composite sensing unit, a non-uniform topological structure is embedded into a seam and an insulating interface area on the surface of the electromagnetic shielding coupler. According to the test system of the test transformer electromagnetic shielding coupler based on intelligent sensing, through dynamic electromagnetic field reconstruction of the flexible distributed intelligent sensing array and multi-band interference coupling modeling, the limitation of a traditional static test is broken through; and real-time capture and multi-dimensional decoupling of shielding effectiveness attenuation characteristics in a complex alternating electromagnetic environment are realized. Compared with a traditional method, the method has the advantages that the shielding effectiveness dynamic quantization error is reduced, and the engineering guidance value of test data is improved.

The invention provides a transformer substation monitoring method and system based on the Internet of Things, and the method comprises the steps: obtaining an original electromagnetic disturbance signal of a transformer substation, and constructing an electromagnetic disturbance feature matrix; carrying out the anomaly detection of the electromagnetic disturbance signal through employing a self-adaptive attention mechanism in combination with asymmetric anomaly detection loss; inputting the time sequence input vector into a fault classification model to perform fault type identification, outputting a fault type label and severity, and performing fault propagation path calculation on the fault type label and severity to obtain a fault propagation path; and simulating fault evolution through a digital twin model, predicting a fault development trend, a secondary fault influence range and a future state vector, and finally generating a maintenance priority, a time window and a resource scheduling scheme. The system breaks through the limitation of a traditional monitoring technology, improves the intelligent level of a transformer substation, reduces the maintenance cost, and improves the operation safety and reliability of a power grid.

Embodiments described herein provide a Transformer architecture for time series data forecasting. Specifically, the Transformer based time series model may be built on a transformer architecture having one or more multi patch size projection layers in the encoder and the decoder, and an any-variate attention module. The Transformer based time series model may receive multivariate time series and consider all variates as a single sequence. Patches of the input are subsequently projected into vector representations via a multi patch size input projection layer. The output tokens of forecasted time series data are then decoded via the multi patch size output projection layers in the parameters of the mixture distribution.

The invention discloses a multi-mode CNN / Transform image defect diagnosis tracking decision-making method, and belongs to the field of power electronic industry detection. According to the method, defect position multi-modal data of different batches, process stages and equipment are collected, after space-time alignment and image enhancement preprocessing and alignment are conducted, features are extracted through position coding, a CNN shallow network and ResNet-18, the features are fused into multi-modal feature vectors in combination with weights, a detection model is obtained through CNN / Transform model training, defect diagnosis and tracking decision making are achieved, and the defect diagnosis and tracking decision making efficiency is improved. The problems of precise detection, positioning, diagnosis tracking and decision-making of internal tiny component changes and structural defects of electronic products and local and overall multi-mode defects of single electric power are solved. The detection accuracy is improved by 20%, the diagnosis accuracy is more than 98.5%, the defect tracking error is less than 5%, the decision-making efficiency is improved by 40%, and the defect prediction capability is improved by 22%.

The invention provides an electric power inspection path planning method and device and electronic equipment, and relates to the technical field of unmanned aerial vehicle electric power inspection. The method comprises the following steps: acquiring obstacle information of an electric power facility environment, wherein the obstacle information comprises the type and position of an obstacle; based on the type of the obstacle and a preset safety distance coefficient, determining a differentiated safety distance, the type of the obstacle including a power transmission line, a transformer substation, a tower and other obstacles; based on the obstacle information and the differentiated safety distance, obtaining an initial global path through a path search algorithm; based on a preset multi-objective optimization function, the initial global path is optimized, a Pareto optimal path set is generated, and the multi-objective optimization function comprises a path length objective, a safety margin objective and an electromagnetic safety objective; and determining a target global path from the Pareto optimal path set based on a preset inspection task mode. According to the invention, the inspection efficiency and adaptability can be improved while the safety is guaranteed.

The invention relates to the technical field of power electronics, and discloses a soft switch of a high-frequency switch transformer and a distributed control method and system thereof, and the method comprises the steps: achieving the precise synchronization of nodes through the construction of a ring topology network, combining the monitoring of multiple physical quantities with the collaborative optimization of parameters, dynamically adjusting the parameters of a resonant network and a driving time sequence, and achieving the precise synchronization of the nodes. A zero-voltage switching state is maintained, and electromagnetic interference is suppressed by adopting the composite optimization model; the system comprises a network initialization module, a multi-physical-quantity monitoring module, a space-time synchronization control module, a resonance parameter adjustment module, an electromagnetic optimization decision module, a dynamic adjustment module and a parameter evolution module. The stability of the soft switch is improved through a distributed network and time-space synchronization, and the loss is reduced by adopting a dynamic optimization algorithm; constructing an electromagnetic interference optimization model to enhance compatibility; performing multi-parameter fusion and weight distribution to optimize dynamic response; the reliability is improved through a high-precision protocol and coevolution; and the tensor product framework realizes multi-dimensional intelligent cooperative control.

The invention relates to a settlement data analysis and early warning method based on a cloud platform, and the method comprises the steps: collecting settlement data of a basic part of a main transformer in real time through a high-precision electronic settlement observation device array, obtaining deformation stress data through data preprocessing and coordinate calculation, and uploading the deformation stress data to the cloud platform; transmitting an ultrasonic pulse signal to the stress concentration position of the local area through ultrasonic detection equipment, receiving reflected wave data, and identifying the depth and range of the crack initiation position according to the reflected wave data; a sensor array is arranged on the basic surface of a crack initiation position, sound wave signals released in the crack propagation process are captured, and a time sequence of the crack propagation direction and speed is determined; and detecting crack depth data of the repair priority region through ultrasonic waves, calculating an adjusted basic health state value, comparing the adjusted basic health state value with a preset safety threshold, and generating a corresponding safety early warning signal.

The invention relates to the technical field of molded case circuit breaker control, and discloses an intelligent molded case circuit breaker control method and system based on big data, and the method comprises the steps: collecting the operation data of a circuit breaker and the related data of a circuit system in real time through a plurality of sensors disposed on the molded case circuit breaker, and obtaining multi-source heterogeneous data; constructing a deep generative adversarial network to carry out abnormal data detection on the collected multi-source heterogeneous data, and processing to obtain preprocessed data; constructing a risk assessment model by adopting a CNN-LSTM-Transform neural network, inputting the preprocessed data into the risk assessment model, and outputting a risk assessment result; according to a risk assessment result, combining an operation environment and a load condition of a current circuit, and based on an adaptive threshold decision algorithm and fuzzy logic control, dynamically adjusting an action threshold of the circuit breaker, and formulating a corresponding control strategy; according to the invention, the control intelligence level and reliability of the circuit breaker are improved.

The invention discloses a water quality prediction method based on a Transform-LSTM fusion model, and belongs to the technical field of water quality time series data prediction and artificial intelligence. Comprising the following steps: (1) acquiring water quality data from a water quality monitoring station; (2) carrying out pretreatment; (3) screening out water quality characteristic data; (4) dividing into a training set, a verification set and a test set; (5) inputting the data into a Transform-LSTM (Long Short Term Memory) fusion model; (6) embedding water quality data time sequence information by a Transformer encoder through position coding, extracting a global dependency relationship among features by utilizing a multi-head attention mechanism, and optimizing gradient propagation by combining residual connection and layer normalization; (7) the LSTM layer receives the high-order features after Transform coding, and captures a local time sequence dynamic mode; and (8) mapping the extracted water quality time sequence characteristics to a specific prediction result by a regression output layer by adopting a linear activation function, and calculating an evaluation index. According to the method, the water quality change trend of the surface water body can be effectively predicted, and powerful support is provided for water ecological protection and sustainable development.

The invention discloses a dam break flow rapid prediction method and a dam break flow rapid prediction system adopting a Transform-ResUNet model. Comprising the steps of obtaining actual dam break data or obtaining a data set for model training through numerical simulation; a dam break flow space-time coupling prediction model fusing Transform and ResUNet is constructed, the dam break flow space-time coupling prediction model is an encoder-decoder architecture, an encoder extracts spatial features of a water flow field through a plurality of residual convolutional layers, then feature maps are partitioned and flattened into time sequence features, and the time sequence features are input into a parallel stacked Transform module for time dynamic modeling; the decoder fuses the spatial features and Transform enhanced time sequence features through cross-layer connection, and reconstructs a predicted water flow field at a future moment through bottleneck residual blocks which are up-sampled and stacked step by step; and training the constructed dam break flow space-time coupling prediction model, and realizing rapid prediction of the dam break flow by using the trained prediction model. According to the method, high-fidelity flow field detail reconstruction is realized in flow field prediction based on an improved ResUNet encoder-decoder structure.

The invention discloses a transformer equipment oil leakage monitoring method and system based on image recognition, relates to the technical field of image processing, and effectively eliminates static background interference by constructing a standardized image sequence S (t), performing gray gradient analysis and pixel feature extraction, recognizing candidate oil spot areas and constructing a disturbance active score Sx. The edge structure and the morphological stability coefficient of the dynamic oil stain suspected area are further extracted, and the credible level quantitative judgment of the dynamic oil stain suspected area is realized by integrating the characteristic indexes and the morphological stability coefficient, so that the dynamic identification capability and the anti-interference capability are realized; the oil leakage identification accuracy and the system automation risk response capability in a complex environment are improved, false alarm and missing alarm are effectively avoided, and the operation safety of equipment is guaranteed.

The invention relates to the technical field of intelligent manufacturing equipment fault diagnosis, and particularly discloses a Meta-Transform driven multi-working-condition equipment bearing fault diagnosis method and system. The method aims at bearing fatigue damage risks caused by dynamic adjustment of technological parameters of a numerical control machine tool in the aerospace manufacturing process and challenges such as feature distribution offset and fault sample scarcity caused by variable working conditions. The diagnosis system is constructed through three core modules. The method comprises the following steps: firstly, reconstructing an original bearing signal into a multi-scale time-frequency feature space by adopting continuous wavelet transform; then designing a causal Transform architecture with a strict lower triangle attention mask, and realizing feature extraction and classification according to a physical causal law of fault propagation; and finally, integrating the mechanisms into a model-independent element learning framework, and realizing cross-working-condition rapid self-adaption through a self-adaption gradient pruning strategy. The bearing fault diagnosis accuracy under the condition of few samples is improved, the interpretability and generalization ability of the model are enhanced, and the industrial application practicability of bearing fault diagnosis is improved.

The invention discloses a transformer electromagnetic thermal field real-time prediction method based on a physical constraint embedded neural network, and belongs to the technical field of transformer monitoring. The method aims at solving the problems that a traditional finite element method is poor in real-time performance, low in precision and weak in pure data driving model generalization. The method comprises the following steps: selecting a load rate, an environment temperature and a shell convective heat transfer coefficient as key parameters, generating a sample by optimal Latin hypercube sampling, and establishing a three-dimensional electromagnetic-thermal-fluid coupling finite element model to construct a training / testing database; constructing a deep full-connection neural network of which the input is five parameters easy to measure and the output is a winding temperature nephogram, and designing total loss function training containing data / physical loss; and deploying an on-line monitoring system after verification is qualified, and collecting parameters in real time to output a winding temperature cloud picture. The method has the characteristics of high precision, strong generalization and easy deployment, and provides support for intelligent operation and maintenance and digital twinning of the transformer.

The invention relates to a rectification harmonic suppression control method for a double reverse star transformer. The method comprises the following steps: establishing a rectifier bridge arm phase characteristic parameter table in a control platform, and recording a theoretical conduction phase, winding attribution and waveform synchronization characteristics of a bridge arm; in the operation process of the rectification control unit, the output current of a transformer and the conduction state of a bridge arm are collected, and a rectification operation state data set is generated; and calculating bridge arm conduction phase deviation, and analyzing a corresponding harmonic component to obtain a mapping relation between a bridge arm state and harmonic distribution. And judging whether the current bridge arm configuration meets the phase coverage requirement of the target rectification pulse number or not according to the mapping result, and if not, selecting a standby bridge arm group with a preset phase difference and generating a switching instruction. And the rectification control unit responds to the instruction to lock the current bridge arm conduction path and release the target bridge arm group so as to realize bridge arm resource dynamic reconstruction and rectification performance optimization. Harmonic waves can be effectively suppressed, and the topology adaptability and the output quality of the rectification system are improved.

The invention relates to the technical field of electrical variable testing, in particular to a high-precision performance testing processing method and system for a low-impedance voltage transformer. The method comprises the following steps: constructing a controllable test excitation signal, and synchronously collecting a corresponding primary side voltage and a secondary side voltage of a mutual inductor; space electromagnetism elimination is carried out according to the primary side voltage and the secondary side voltage of the mutual inductor, and frequency domain characteristic decomposition and signal instantaneous offset calculation are carried out at the same time, so that a mutual inductor signal characteristic matrix is constructed; acquiring environment parameters corresponding to the low-impedance voltage transformer; constructing a neural network model based on Bayesian optimization, and predicting and outputting a performance parameter set corresponding to frequency response and load characteristics; and comparing the performance parameter set with a preset standard threshold value, feeding back, generating a calibration compensation coefficient, testing and evaluating, and generating a high-precision performance test report corresponding to the low-impedance voltage transformer. According to the invention, high-precision performance testing of the low-impedance voltage transformer can be realized.