18138 results about "Transformer" patented technology

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 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 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-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 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 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 discloses a power system net load prediction method based on regular decomposition and double-branch prediction, and the method comprises the steps: obtaining a historical net load sequence of a target power system and corresponding environment parameters, constructing a target function fusing fitting precision and trend smoothness through employing a regularization optimization method, extracting a long-term trend sequence of a net load, and carrying out the calculation of the long-term trend sequence. And a short-term disturbance sequence is separated. Constructing a trend prediction sub-network based on series connection of a Transform encoder and a long-short-term memory network, and learning a trend evolution rule; meanwhile, a regression prediction sub-network based on environmental parameters is constructed, and a nonlinear mapping relation between disturbance and environmental factors is modeled. And utilizing the two types of sub-networks to respectively predict future trend and disturbance components and superpose the future trend and disturbance components to obtain a multi-time-step net load prediction result. According to the method, the problem that a traditional model is insufficient in trend and disturbance modeling capacity is effectively solved, and the accuracy and stability of load prediction are improved.

The invention provides an electric power technology service guarantee supervision method, which comprises the steps of determining a matching degree evaluation criterion of an operation tool specification and a protection measure grade according to a real-time operation data set, and meanwhile, integrating a compliance deviation reason according to an out-of-plan repair judgment result to determine an operation rationality evaluation criterion; performing electric power risk grading analysis on the cascading failure prediction set, judging the existence condition of a high risk grade, and extracting transformer abnormal sign data to obtain a risk grade classification result; according to the flow difference degree of the optimized repair path and the initial repair operation step, the optimized path is integrated back to the real-time operation data set, and meanwhile, in combination with transformer abnormal sign data, an update operation guidance set is obtained; and generating a power operation visualization report according to the updated operation guidance set, verifying the applicable condition that the alarm is not directly started when the service standard process is deviated, and obtaining a final compliance judgment conclusion.

The invention belongs to the technical field of electric power system information processing, and particularly relates to an electric power information analysis method based on big data, through semantic fusion of multi-source heterogeneous data and dynamic feature mining of a time sequence attention mechanism, in a load prediction scene, compared with a traditional single data source model, the electric power information analysis efficiency is improved. After the meteorological data, the user power consumption behavior data and the power grid operation data are fused, the prediction average error rate is reduced; in an equipment fault early warning scene, through multi-dimensional correlation analysis of vibration signals, oil temperature data and environmental factors, transformer latent faults can be early warned in advance, and the fault identification accuracy is improved; meanwhile, a self-adaptive modeling engine and a closed-loop feedback mechanism enable the system to have a self-evolution capability: when the power grid topology is adjusted or the new energy grid-connected proportion is changed, the model does not need to be manually retrained, and self-adaptive adaptation can be completed in two scheduling cycles through dynamic feature weight adjustment and meta-learner parameter optimization, so that the analysis performance is maintained to be stable.

The invention discloses a pole-mounted transformer state evaluation and fault early warning method fusing multi-source data, and relates to the technical field of intelligent power grid monitoring, and the method comprises the steps: executing dual state judgment according to a multi-source fusion feature set, extracting a statistical deviation degree of operation fluctuation abnormity, and calculating a physical deviation degree of multi-physical field coupling imbalance; the statistical deviation degree and the physical deviation degree are fused in a double-domain cooperation mode, and a real-time health index is obtained; the degradation rate and multi-scale energy characteristics of the real-time health index are extracted, a potential degradation aggregation mode is identified by referring to a historical slow-varying baseline, and a hidden danger trend factor is generated; performing composite attribution matching on the hidden danger trend factor and the multi-source fusion feature set, executing grading risk criteria according to a multi-physical field anomaly mechanism chain, and outputting an early warning grade and an early warning reason; according to the method, diagnosis is carried out by embedding a multi-physical field anomaly mechanism chain, and the physical root and evolution stage of the fault can be accurately positioned.

The invention provides a fault detection method, device and equipment for a direct current transformer with variable magnetic flux density, and the method comprises the steps: obtaining a magnetic field recovery curve family through gradient pulse excitation, and extracting a characteristic spectrum which reflects the fatigue degree of an iron core; separating fast and slow recovery components by using a time scale decomposition technology, calculating a scale coupling factor and establishing a dynamic health reference; a hysteresis critical point is positioned by adopting a bidirectional approximation test, and a fault precursor is identified from hysteresis width parameters; extracting an intersection point position through curve family cross analysis, constructing an angle field, calculating an angle sequence, and quantifying a fault development speed; designing a self-adaptive excitation scheme based on the development speed, and identifying a fault resonance mode from the chaotic transition features; a multi-resolution space fault distribution diagram is generated through phase space trajectory deviation degree analysis; spatial distribution and time sequence evolution information are fused to calculate a comprehensive fault index, four-stage classification diagnosis is realized, and a complete mutual inductor fault detection technical scheme from early warning to accurate positioning is formed.

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 field of oil-immersed transformers, in particular to an oil-immersed transformer operation risk assessment method and system, and the method comprises the steps: obtaining real-time operation parameters and external influence factors of an oil-immersed transformer; the real-time operation parameters and the external influence factors are preprocessed; based on the preprocessed real-time operation parameters and the external influence factors, outputting a comprehensive risk assessment score; calculating a transformer health index based on the preprocessed real-time operation parameters and the external influence factors, and calculating a risk occurrence probability according to the transformer health index and the comprehensive risk assessment score; and outputting a risk level report based on the comprehensive risk assessment score, the transformer health index and the risk occurrence probability, and generating an early warning signal. According to the method, the aging factor is introduced, and the calculation weights of the comprehensive risk score, the health index and the risk probability are adjusted in combination with the service life of the transformer, so that the evaluation model can dynamically adapt along with the aging degree of the equipment, and the evaluation precision of the transformer is improved.

The invention discloses a loss tuning method of a power transformer, which is applied to a transformer body sleeved with a winding and comprises the following steps: applying scanning current excitation containing fundamental waves and harmonic waves to the winding, synchronously acquiring a body vibration signal and converting the body vibration signal into a frequency spectrum; extracting a formant from the frequency spectrum, matching the formant with a theoretical electromagnetic force wave and a structure inherent frequency library, and identifying a coupling formant to be optimized; aiming at each formant, installing a vibration exciter in a corresponding area, sending out an anti-phase periodic pulse force, and dynamically and finely adjusting a pulse force parameter by monitoring a vibration response in real time and taking equivalent mechanical impedance minimization as a target; when the optimal damping state is achieved, the vibration exciter output rod is locked, and static pre-tightening force is formed; and after all formants are adjusted and optimized in sequence and the prestress is locked, final pressing and fixing of the transformer body are completed in the state that the pretightening force is kept. According to the invention, the dynamic loss source of the individual transformer can be actively inhibited and cured before assembly and curing, the operation loss and noise are effectively reduced, and the structural stability is improved.

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 relates to the technical field of power generation, power transformation or power distribution, and discloses a control method for preventing overload of an energy storage system transformer, which comprises the following steps: monitoring the three-phase total power and the power of each phase of a power grid side ammeter in real time, and calculating the three-phase unbalance degree based on the three-phase total power when the three-phase unbalance phenomenon occurs; when the three-phase unbalance degree is greater than a preset unbalance degree threshold value, dynamically adjusting the charging power of the energy storage converter according to a starting threshold value, a charging limiting threshold value and a charging forbidding threshold value by adopting a multi-threshold value dynamic adjustment mechanism; the phase with the maximum deviation between the power of each phase and the average power of the three phases is calculated as an overload phase, load reduction is performed on the overload phase based on a load increasing and decreasing strategy, load increasing is performed on a non-overload phase, and the adjusted three-phase power is obtained; a power distribution instruction is calculated in real time in combination with a dynamic response mechanism, and the three-phase unbalance degree does not exceed a safety threshold value in cooperation with total power control. The problems of easy overload, three-phase imbalance and slow response of a transformer in the prior art are solved, and the purposes of accurate load control, high system stability and high response speed are achieved.

The invention discloses a dry-type transformer. The dry-type transformer comprises a transformer body, a housing, a bottom plate and a fan, wherein the transformer body is formed by a plurality of groups of encapsulated windings in an array mode, a supporting rod is arranged at the bottom of the transformer body and allow the encapsulated windings in each group to connect with each other, the supporting rod is arranged in a crossed mode, and first threaded holes penetrate the supporting rod; the transformer body is integrally arranged in the cavity of the housing, and the bottom of the transformer body is fixedly positioned with the bottom plate, and meanwhile, the transformer body is arranged in the housing through the fan, the whole device achieves dispelling the heat in the transformerbody, and the graphite material of housing side is protruding simultaneously, and the heat from the housing is diffused quickly, and the protrusion of the array distribution can reduce the air great dust entering the inner wall of the housing through air vents.

The invention relates to the technical field of electric power fault diagnosis, and discloses a transformer area line loss abnormity diagnosis method and system based on a large model, and the method comprises the steps: collecting multi-dimensional data used for supporting transformer area line loss abnormity diagnosis, carrying out the cleaning, correlation fusion and standardization processing of the multi-dimensional data, and obtaining a comprehensive data set; a multi-layer diagnosis system based on a rule model, a random forest model and a large model is constructed, and the rule model identifies the simple and conventional anomalies of the transformer area according to a preset anomaly diagnosis rule based on the basic attribute data and the power operation state data in the comprehensive data set; the random forest model locates complex anomalies and novel anomalies by mining a coupling relationship among energy access condition data, external environment influence data and line loss fluctuation in the comprehensive data set; the big model carries out fusion verification on diagnosis results of the rule model and the random forest model, and outputs a final transformer area abnormity diagnosis result; and based on the diagnosis result, a differential loss reduction strategy adaptive to the actual data characteristics of the transformer area is recommended. The working efficiency is improved.

The invention relates to the technical field of power system testing, in particular to a transformer simulation test current optimization method based on dynamic impedance matching, which comprises the following steps: performing low-power scanning on a tested transformer to establish a nonlinear model capable of predicting harmonic waves; actively synthesizing the virtual impedance of the target power grid; and a feed-forward compensation current is generated by using a harmonic prediction model and is injected into the transformer after being superposed with a fundamental wave instruction. According to the method, accurate identification modeling is carried out on nonlinear characteristics of a tested unit, active synthesis of virtual impedance of a target power grid is combined, and harmonic compensation current is generated in real time by utilizing a feedforward control thought so as to optimize finally injected test current. According to the method, the problems of inaccurate power grid simulation and insufficient nonlinear response coupling of the transformer in a traditional test are solved, high-fidelity current injection is realized, real electrical behaviors of the transformer under various complex power grid working conditions can be accurately simulated, and the flexibility, accuracy and safety of the test are remarkably improved.

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.