2319 results about "Instrument transformer" patented technology

The invention belongs to the technical field of simulation analysis, and particularly relates to a medium-high voltage switch cabinet internal humidity inversion calculation method based on simulation analysis, which comprises the following steps: establishing a geometric model of a switch cabinet, importing the geometric model into finite element simulation software to obtain a simulation analysis model, adding each physical field and computational domain and boundary conditions thereof, and calculating the internal humidity of a medium-high voltage switch cabinet. All the physical fields are coupled, and mesh generation is carried out; performing numerical solution on the simulation analysis model by using a transient solver to obtain a temperature and humidity distribution condition in the switch cabinet; the simulation analysis model is optimized, a simulation data set is constructed, and a PINN is trained; based on the environment temperature and humidity and the operation current collected in the actual operation of the switch cabinet, the trained PINN is used for prediction, and the steady-state relative humidity of the current transformer is obtained. According to the invention, based on the limited boundary monitoring data, efficient and accurate reconstruction of the humidity field of the key area in the switch cabinet is realized, and a feasible path is provided for equipment state sensing and condensation risk early warning.

The invention discloses a method for detecting abnormal metering performance of an intelligent electric energy meter, which belongs to the technical field of electric energy metering equipment and comprises the following steps of: 1, establishing a reference response curved surface of parasitic parameters of a voltage sampling resistor and a current transformer in the electric energy meter relative to temperature and humidity; step 2, during the operation period of the electric energy meter, acquiring the real-time measurement value of the parasitic parameter in situ; and step 3, matching the real-time measurement value with the reference response curved surface. According to the method, an active matching model library can be constructed by fusing an underlying physical model of a component and a mapping rule set for a complex environment with sudden change of plateau outdoor temperature and humidity, the internal mechanism that parasitic parameters change along with temperature and humidity is explained from the physical essence level, and the limitation of a pure data statistical method is made up; the dynamic error judgment threshold value can adapt to environmental stress changes in real time, and the accuracy of metering performance anomaly detection and the complex environment adaptability are effectively improved.

The invention provides an intelligent monitoring and early warning method based on multi-source information fusion, and belongs to the technical field of intelligent diagnosis and early warning, and the method comprises the steps: synchronously collecting the multi-modal data of a voltage transformer, the multi-modal data comprising voltage / current waveform, partial discharge signal, temperature and vibration data; preprocessing the multi-modal data, wherein a preprocessing method comprises waveform segmentation, spectrogram generation and scalar normalization; constructing a dynamic weight distribution mechanism: calculating a weight matrix based on the relevance between the time sequence features and the spatial features, and dynamically adjusting scalar weights by combining the physical coupling relationship between the temperature and the vibration to generate feature fusion weights; iteratively updating the weight matrix according to the real-time prediction error; fusing the time sequence features, the spatial features and the scalar features through the dynamic weight distribution mechanism to generate a comprehensive feature vector; and synchronously outputting a fault type classification result and an equipment health score based on the comprehensive feature vector, and calculating a real-time prediction error.

The invention relates to the technical field of voltage transformers, in particular to a voltage transformer real-time monitoring system based on broadband and edge calculation, which comprises the steps of collecting multiple paths of voltage transformer data, extracting peak values and zero cross points, comparing jump rising edge time differences, unifying time sequences, extracting extreme values and slope identification disturbance sections, and monitoring the voltage transformers in real time. Measuring amplitude continuous change to judge the offset trend, analyzing trend direction classification waveform fragments, and combining the three indexes to generate an operation state label. According to the method, a unified time sequence is constructed by comparing cross-channel jump rising edge time differences, multi-channel jump process alignment is achieved, the synchronous anomaly recognition capability is improved, the number of extreme values and the slope direction are extracted to recognize a disturbance dense section, continuous anomaly is judged by combining statistics of overrun, and the fluctuation trend is judged by tracking the deviation trend between extreme values. And based on the three-index combination classification operation state, the anomaly identification precision is improved.

The invention relates to the technical field of power system relay protection, and discloses a relay protection device mutual inductor adjustment system and method, and the system comprises a system connection initialization module, a data collection reference data generation module, a power frequency background signal offset module, a pilot frequency current injection error calibration module, and a calibration result verification output module. The method comprises the following steps: initializing sampling frequency of a to-be-calibrated mutual inductor and calibration source output precision of a calibration system, collecting amplitude and phase data of real-time power frequency current of the mutual inductor, generating offset current with equal amplitude and opposite directions through power electronic compensation based on reference data, optimizing offset current parameters through a closed-loop adjustment algorithm, and adjusting the offset current parameters through a closed-loop adjustment algorithm. Power frequency background signal offset is completed, pilot frequency calibration parameters are set, a difference value between a mutual inductor pilot frequency output signal and a calibration source standard signal is extracted by adopting a zero pointing technology, an optimal calibration coefficient is automatically calculated, verification of a calibration result is completed, and accuracy and reliability of the calibration result are ensured.

The invention relates to the technical field of power data analysis, in particular to a line loss abnormity associated mutual inductor insulation fault positioning method. Collecting multi-source operation data of a target station area, and obtaining a datum line loss rate sequence, a residual component sequence and an auxiliary feature vector based on the multi-source operation data; according to the reference line loss rate sequence, obtaining a comprehensive sensitivity vector representing the influence degree of the mutual inductor on the line loss; obtaining a preliminary diagnosis result according to the comprehensive sensitivity vector, the residual component sequence and the real-time load rate; performing spatial logic verification according to the preliminary diagnosis result and the electrical topological graph of the transformer area to obtain a fault positioning list; generating a correction amount according to the fault positioning list and the corresponding comprehensive sensitivity vector, and correcting the line loss rate according to the correction amount; and meanwhile, feedback optimization is performed on the machine learning model according to complete data of the diagnosis. According to the invention, accurate and efficient diagnosis and correction of the line loss abnormity of the target station area can be realized.

The utility model relates to the field of resonance eliminators, and discloses a protection element and a current limiting type resonance eliminator, the protection element comprises at least two groups of protection units, the two adjacent groups of protection units are connected in series, and each protection unit comprises a protection module; the current-limiting resonance eliminator based on the current-sensitive resistor made of the current-sensitive material is connected in series between the PT neutral point and the ground. Under the normal operation condition, the current-limiting resonance eliminator is in a low-resistance state, and system parameters and PT performance are not affected; when excitation energy is generated due to a system fault, the current-limiting resonance eliminator suddenly changes into a high resistance state, quickly absorbs resonance energy, destroys resonance parameters, suppresses PT excitation current below 200mA, and ensures that a voltage transformer is unsaturated and a PT fuse is not fused; and a built-in current sensor is connected, so that the running state of the current-limiting resonance eliminator can be monitored in real time.

The invention discloses a GNN-PINN fusion dynamic error enhancement current transformer error prediction method, and the method comprises the following steps: firstly, building a sliding window steady-state recognition method based on a kurtosis-entropy collaborative criterion, and achieving the precise segmentation of current features under a complex working condition through multi-dimensional statistic fusion; secondly, a dynamic error simulation injection mechanism is designed, a current data enhancement strategy guided by a physical rule is constructed, and the working condition coverage of training data is effectively improved; on the basis, a GNN-PINN-based current transformer error prediction model is provided, and deep coupling of an error mechanism and data characteristics is realized through embedding a physical connection relation of equipment through a topological graph and combining partial differential equation constraints, so that a current transformer error is obtained. According to the current transformer error prediction method provided by the invention, the key problems of difficulty in steady-state feature extraction, scarcity of data samples, lack of physical constraints and the like in current transformer error prediction during online calibration of the transformer substation current transformer can be solved.

The invention discloses an automatic voltage regulating device for a power distribution area and a control method of the automatic voltage regulating device, and belongs to the technical field of power distribution network voltage treatment. The device comprises a three-phase power supply, a power supply side input circuit, a load side output circuit, a current transformer, a single-P air switch, a power supply conversion module, a three-phase independent voltage regulation module, a main path control system, an automatic bypass control system, a gear driving module, a man-machine interaction module, a display module, a monitoring module, a mode switching module, a parameter storage module and an alarm module. The system comprises a manual adjustment execution module, a counting module, a voltage detection module, a current detection module, a 4G communication module and an over-temperature protection module. A main circuit control system depends on input and output voltage data fed back by a voltage detection module, compensation voltage U can be independently calculated for differential voltage fluctuation of L1, L2 and L3 phases, intelligent adjusting switches of all phases are driven to act accurately, and finally three-phase output voltage U2 is stabilized within an allowable range.

The invention discloses a voltage transformer state influence factor analysis method, system and device and a medium, and belongs to the technical field of voltage fault analysis, and the method comprises the steps: collecting state parameters of a voltage transformer, carrying out the preprocessing of the state parameters, and constructing a nonlinear coupling relation between multidimensional feature tensor capture features; analyzing voltage fluctuation characteristics of the voltage transformer, and automatically correcting the insulation state evaluation model; and inputting abnormal feature vectors obtained by monitoring into a recursive attribution decision tree model, carrying out multi-dimensional abnormal clustering and causal analysis, generating a tracing report, and carrying out multi-dimensional evaluation and verification on an analysis result. According to the invention, continuous dynamic full-coverage monitoring of the equipment insulation health condition is realized, the accuracy and robustness of anomaly detection are improved, the phenomena of missing detection and false alarm are reduced, the scientificity of anomaly tracing analysis and the interpretation of early warning decision are improved, and the reliability of the system is improved. And the intelligent level of the monitoring and early warning system is improved by continuously adapting to new abnormal types and complex operation environments.

The invention discloses a J-A model parameter identification method, system, equipment and medium based on RBF and an improved brownish bear algorithm, and belongs to the technical field of power system optimization, and the method comprises the steps: building a Jiles-Atherton hysteresis reverse model of a current transformer, determining a to-be-identified parameter vector, and building a model with a root-mean-square error between actually measured magnetic field intensity and simulated magnetic field intensity as a target function, training a radial basis function neural network model, expanding data through linear interpolation processing, obtaining a predicted magnetic induction intensity value, inputting an objective function and radial basis function prediction data into an improved brownish bear optimization algorithm, and iteratively optimizing model parameters through hierarchical population position updating and fitness evaluation until convergence conditions are met. And outputting an optimal parameter identification result. According to the method, high-precision and high-efficiency identification of hysteresis model parameters is realized, the generalization capability and robustness of the system are improved, and reliable technical support is provided for hysteresis characteristic analysis of a complex physical system.

The invention relates to the technical field of optical current transformer detection, and provides an optical current transformer real-time calibration and health diagnosis method and system, and the method comprises the steps: carrying out the harmonic analysis of an initial digital signal sequence obtained through the conversion of a continuous modulation optical signal of a target optical current transformer, and obtaining harmonic component data; physical state evaluation is carried out according to the higher harmonic component data and preset health state baseline data to generate a physical state evaluation result, nonlinear disturbance compensation is carried out according to an error control signal generated based on the higher harmonic component data, and after a linear working point is reached, nonlinear disturbance compensation is carried out; and calculating an electrical measurement value according to the output target fundamental component data, and performing comprehensive analysis according to the physical state evaluation result and the electrical measurement value to generate a diagnosis report. According to the invention, through a measurement calibration and state diagnosis mechanism based on harmonic analysis, while the electrical performance measurement precision is effectively improved, reliable quantitative diagnosis and accurate traceability of different physical faults in the equipment are realized.

The invention relates to the technical field of wiring robot system fault detection, in particular to a transformer wiring robot system fault intelligent detection method. The method comprises the following steps: acquiring an image in a wiring process and graying the image to obtain a grayscale image; obtaining candidate pixel points in the grayscale image and a pixel point sequence of the candidate pixel points; acquiring a local gradient amplitude change feature value and a local gradient direction change feature value of each neighbor pixel point, and further acquiring initial edge feature credibility of the candidate pixel points; obtaining the final edge feature credibility based on the initial edge feature credibility of each marked pixel point of one candidate pixel point; performing clustering analysis based on the final edge feature credibility of each candidate pixel point in a gray level image to obtain a low threshold value and a high threshold value of the gray level image; and carrying out edge detection on the grayscale image by using the low threshold and the high threshold, and identifying the abnormity of the wiring robot. According to the invention, the wiring abnormity of the wiring robot can be effectively processed.

The invention provides a PT ferromagnetic resonance overvoltage suppression system and method based on graded damping. The system comprises a secondary resonance elimination module, the secondary resonance elimination module comprises a three-stage damping branch circuit which is connected in series with an open triangle of a voltage transformer, the three-stage damping branch circuit comprises a first current-sensitive resonance eliminator, a second current-sensitive resonance eliminator and a fixed resistor which are connected in parallel, and a branch circuit of the first current-sensitive resonance eliminator is connected in series with a first switch; a branch of the second flow-sensitive resonance eliminator is connected in series with a second switch, and a branch of the fixed resistor is connected in series with a third switch; the fan is used for carrying out air cooling on the first flow-sensitive resonance eliminator, the second flow-sensitive resonance eliminator and the fixed resistor; and the control module is used for controlling on or off of the first switch, the second switch and the third switch. Resonance early recognition is achieved through transient energy comprehensive criteria, three-level damping dynamic switching and fan intelligent temperature control are combined, and the three core problems of response lag, insufficient heat dissipation and weak self-adaption in ferromagnetic resonance suppression are solved.

The invention provides a new energy station high-voltage voltage transformer error evaluation method and system, and the method comprises the steps: collecting the voltage phasors of all measurement nodes in a preset time period at a transformer substation and a new energy station at the same time, obtaining the equipment parameters of a voltage transformer and the line parameters between the measurement nodes, and constructing a measurement data set; the method comprises the following steps: constructing a corresponding adjacency matrix and a node characteristic matrix by extracting line impedance and node working condition characteristics based on a power system line topological structure and data in a measurement data set, and obtaining a time sequence diagram structure data set according to the adjacency matrix and the node characteristic matrix; constructing a graph neural network, and training the graph neural network based on the time sequence graph structure data set; and inputting the voltage phasor of each measurement node, and based on the output result of the trained graph neural network, judging the state of the voltage transformer through a preset threshold value. According to the scheme, online evaluation of the error state of the voltage transformer can be realized, and the accuracy and interpretability of error evaluation are improved.

The invention provides a primary resonance elimination device of a power distribution network voltage transformer and a management and control method, and relates to the technical field of voltage transformer resonance elimination. The primary resonance elimination device comprises a primary resonance elimination resistor, a primary resonance elimination power electronic device, a standby resonance elimination resistor and a standby resonance elimination power electronic device; the primary resonance elimination resistor and the primary resonance elimination power electronic device are connected in series and then are connected in series between a neutral point of the power distribution network voltage transformer and a ground wire; the standby resonance elimination resistor and the standby resonance elimination power electronic device are connected in series, one end of the standby resonance elimination resistor and the standby resonance elimination power electronic device which are connected in series is connected with a neutral point of the power distribution network voltage transformer, and the other end is grounded; the primary resonance elimination device further comprises a current transformer and a temperature sensor; the temperature sensor is used for monitoring the temperature values of the primary resonance elimination resistor and the standby resonance elimination resistor; the current transformer is assembled on a line where the primary resonance elimination resistor and the standby resonance elimination resistor are grounded together and used for monitoring the value of current flowing through the primary resonance elimination device.

The invention relates to the field of electric power measurement, and provides a voltage transformer error evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting the voltage phasor information of a measurement node in a preset time window, and obtaining the reference technical parameters of a voltage transformer corresponding to the measurement node; feature extraction based on a sliding window is carried out, sequence diagram structural features are obtained in combination with an adjacent matrix, and the adjacent matrix is constructed based on network topology and line electrical parameters of a power system; error estimation is carried out through a trained error estimation model based on the time sequence diagram structure features, an error estimation value is obtained, and the error estimation model is obtained by training a graph neural network model based on historical voltage phasor information and corresponding error labels; and performing error state evaluation based on the error estimation value. According to the method, the device, the electronic equipment and the storage medium provided by the invention, online continuous evaluation of the voltage transformer error is realized, and the limitations of long period and high cost of a traditional offline verification method are broken through.

The invention discloses an integrated current transformer fault diagnosis system and method based on machine learning, and relates to the technical field of fault diagnosis. Comprising a current transformer abnormity primary judgment module, a primary judgment abnormal current transformer state label judgment module, a disturbed current transformer disturbed data updating module and a fault source current transformer fault diagnosis module. According to the method, the feature vectors are constructed, and the multi-task gating neural network model is combined to carry out label probability determination, so that the body fault and the disturbed abnormity of the current transformer can be effectively distinguished, and corresponding diagnosis or data updating branches are automatically triggered for different fault types; under the complex working conditions of external electromagnetic interference, load fluctuation and the like, disturbed channel data are self-corrected in real time, so that the stability and the consistency of an overall data set are recovered, the misjudgment rate and the omission ratio are reduced, the robustness of transient disturbance and the fault positioning precision under a conjoined structure are improved, and the fault positioning efficiency is improved. And finally, reliable operation and long-term stability of the power grid measurement and protection system are ensured.

The invention discloses a voltage and current synchronous measurement method for a high-voltage power supply of a fusion device, which is suitable for accurately sampling and monitoring voltage and current signals of each key node of the high-voltage power supply of the fusion device. According to the method, firstly, a resistance-capacitance voltage division technology is adopted, a front-end high-voltage attenuation network is constructed by utilizing high-precision resistors and capacitors, and stable voltage division and linear attenuation of high-voltage signals are achieved; then, electrical isolation is realized through a small-signal voltage and current transformer, and multi-channel synchronous acquisition of voltage and current signals is completed respectively. Then, the ADC module adopts two multi-channel AC sampling special electric energy metering chips to realize ten-channel synchronous acquisition of high voltage and current signals. And finally, the FPGA receives, processes and analyzes the acquired data in real time, and transmits a result to an upper computer monitoring platform. According to the method, the synchronism, the precision and the anti-interference performance of a fusion power supply high-voltage measurement system are remarkably improved, and reliable data support is provided for safe and stable operation of a fusion device.

A hardware-in-the-loop test method and system for a protection device. The method comprises: constructing a simulation scenario in a hardware-in-the-loop simulation test system; on the basis of the mounting position of a protection device (4) to be verified in the simulation scenario, determining the minimum sampling signals required for said protection device (4) to work normally, and establishing a current transformer model capable of restoring a transient saturation scenario; performing a power-on operation on said protection device (4), and enabling the hardware-in-the-loop simulation test system to output an electrical signal; and after a correct sampling reading is acquired on said protection device (4), simulating a fault scenario in the simulation scenario, and on the basis of a current signal having transient saturation characteristics calculated by a model and a logic determination of the protection device, determining whether an action result of the fault scenario satisfies a setting value and an action logic. The present hardware-in-the-loop test method for a protection device restores a true current in a non-ideal fault condition, thereby deeply testing the working characteristics of a relay protection device in a power station.

The invention belongs to the technical field of nuclear power, and particularly relates to a ground capacitance measurement method based on series capacitance and high-frequency signal injection. In order to solve the problem that a traditional signal injection method cannot accurately calculate the ground capacitance level of a power distribution network, the invention provides a ground capacitance measurement method based on a series capacitor and high-frequency signal injection. The leakage inductance of the voltage transformer is accurately measured by injecting the high-frequency signal, so that a resonance point is convenient to find during frequency sweeping, and the resonance point measured during frequency sweeping is more accurate.

The invention discloses a cable warning pile and method for monitoring the state of an intermediate joint of a directly-buried power cable, relates to the field of state monitoring of power equipment in power engineering, and aims to solve the problems of single-parameter acquisition, large interference influence, non-objective evaluation and non-visual early warning in the existing monitoring technology. The warning pile is vertically buried at the position 0.3-0.8 m above a connector, temperature, partial discharge, vibration and environment data are synchronously collected through multiple sensors such as an optical fiber temperature sensor and a high-frequency current transformer, after data alignment, abnormal value elimination, filtering and integral preprocessing, the real temperature of the connector is restored through an environment and vibration coupling correction model, and the real temperature of the connector is obtained. And extracting a partial discharge peak factor and an average discharge capacity core characteristic. An index weight is determined based on an entropy weight method to construct a health degree evaluation model, operation states are divided into normal, attention, early warning and fault classes, hierarchical visual warning is realized through a three-color indicating lamp group and a buzzer, and the model is dynamically updated in combination with maintenance data every three months.

The invention relates to the field of force-electromagnetic coupling measurement, and discloses a low-voltage electric energy metering box internal arrangement method and system based on a modular mutual inductor plugging structure, and the method comprises the steps: synchronously collecting three-phase module magnetic flux density, base strain field and pre-tightening force time sequence data through deploying a multi-source sensor; a directed weighted hypergraph is constructed to represent a magnetoelastic coupling topology, the topology is converted into a time-varying creep rate matrix through a viscoelastic creep memory operator, a compensation strategy is generated in combination with phase space division and a magnetostriction response tree, and finally an optimization scheme of module spacing and interface rigidity is reversely mapped. By accurately capturing the time-varying mechanical law of the strain field and the pre-tightening force, the problem of time-varying coupling drift of the modular mutual inductor caused by structure-electromagnetic coupling and viscoelastic memory effect is solved, the limitation of traditional static independent calibration is broken through, and long-term stability of three-phase metering precision is achieved.

The invention relates to the technical field of power system monitoring, discloses an anti-harmonic PT current monitoring system and method, and aims to solve the problem of secondary side current signal distortion of a voltage transformer caused by power grid harmonic pollution in the prior art. The method comprises the steps of electrifying and initializing a system, starting a high-speed analog-to-digital converter to acquire a voltage signal, executing fast Fourier transform to identify dominant harmonic waves, calculating and configuring a capacitance value of a tunable LC trap unit to suppress specific harmonic waves, and finally outputting a high-fidelity analog voltage signal in direct proportion to fundamental current. The system comprises a current-voltage conversion module, a tunable LC notch filter array, a high-speed analog-to-digital converter, a digital signal processing unit and a microcontroller. According to the technical scheme, dynamic self-adaptive suppression and precise selective harmonic wave blocking of power grid harmonic waves can be realized, low delay and high fidelity characteristics are guaranteed, and the engineering practicability and reliability of the system are improved.

A motor leakage current detector and / or a heat compensating circuit, devices using same and related methods are disclosed herein. Included are a uniquely wired current transformer to allow for polarity agnostic leakage current detection, a leakage current detector using same and having a notifier to alert a user. In other forms, an accessory power cord and power strip are disclosed capable of detecting early motor failure conditions. In still other forms, other machinery failure early warning systems are disclosed as are numerous motor operated devices using same including without limitation pumps.

The invention discloses an error state prediction method and system for a direct-current voltage transformer, and relates to the technical field of electric power measurement on-line monitoring, and the method comprises the steps: obtaining the error data of a target direct-current voltage transformer; segmenting the error data by using a two-order sliding window; performing feature extraction on the time sequence matrix by using fast Fourier transform to obtain a time-varying feature and a time-invariant feature; fusing the time-varying features and the time-invariant features by using a multi-layer perceptron to obtain fused features; and inputting the time-varying features, the time-invariant features and the fusion features into an error prediction model constructed based on deep learning to obtain an error prediction state of the target DC voltage transformer. According to the method, through the synergistic effect of the adaptive window, feature extraction and fusion and sliding value correction, the adaptability, accuracy and stability of error prediction of the direct-current voltage transformer are improved, and more reliable technical support is provided for online monitoring and abnormal early warning of a power system.

The invention discloses a current transformer based on decoupling compensation and an adjusting method thereof, and relates to the technical field of current measurement. The method comprises the following steps: establishing an error coupling matrix by data acquired by a current transformer; a decoupling compensation hardware module is designed, and decoupling compensation is carried out on the exciting current, the secondary load and the transient component; establishing a working condition parameter set, and constructing a training sample library by taking the working condition parameters as input and the compensation coefficients as output based on a BP neural network; through a clock synchronization module, it is ensured that output signals of the three compensation units and current signals of the secondary side of the mutual inductor are the same in frequency and phase; different working conditions are simulated through the standard current source, and the adjusted precision is recorded. According to the invention, through multi-dimensional error decoupling and combination of the BP neural network and the PID, hardware shunting and algorithm optimization are utilized to realize separated compensation of three errors, the method is adaptive to complex working conditions, and the adjustment precision of the compensation circuit is improved.

The embodiment of the invention provides a metering error analysis method and device for a power transformer and an integrated test system, and the method comprises the steps: obtaining equipment attribute parameters of the power transformer, and constructing a multi-physical field coupling model which comprises an electromagnetic field, a temperature field and a mechanical stress field based on the equipment attribute parameters; multi-modal test data of the power transformer are obtained in real time through the integrated test platform; based on the multi-modal test data and the multi-physics field coupling model, constructing a digital twinborn model; calculating an error at the current moment in real time based on the digital twinborn model and the multi-modal test data; based on the digital twin model and a preset error prediction model, error trend evolution and prediction are carried out, and prediction error data of the power transformer in a preset time period are determined; and performing error compensation calculation based on the error at each moment and the predicted error data, and determining an error compensation scheme of the power transformer so as to compensate the metering error of the power transformer and improve the accuracy of error analysis.

A three-phase adaptive reclosing method and system for wind farm transmission lines based on parallel reactor current is provided. When a phase to phase or three-phase fault occurs in the transmission line, the circuit breakers at both ends trip. The current transformer is used to sample the three-phase current signal of the parallel reactor, calculate the differential mode current of the parallel reactor, and perform zero crossing detection to construct a fault nature identification criterion. The fault nature is determined within the maximum discrimination time limit combined with the sliding time window. For permanent faults, the reclosing device is locked; for transient faults, calculate the time when the fault disappears and determine the reclosing time, and open the reclosing device. The method and system identifies the nature of faults by detecting whether the differential current of parallel reactors crosses the zero axis, with low sampling rate requirements, and small errors.

The invention provides an online power supply switch mutual inductor fault detection method and system, a medium and a product, and relates to the technical field of online monitoring and fault diagnosis of power equipment, and the method comprises the steps: generating a synchronous triggering instruction at the zero crossing point moment of the power frequency current of a mutual inductor; in response to the instruction, injecting an excitation pulse into a secondary circuit of the mutual inductor and synchronously acquiring a feedback voltage to obtain a mixed transient signal; extracting a pure response waveform only containing a high-frequency component from the mixed transient signal to carry out multi-resolution time-frequency transformation, and calculating and outputting a multi-dimensional feature vector representing a winding state; inputting the multi-dimensional feature vector into a pre-trained neural network model for classification operation to obtain a fault classification probability of the current transformer state; and comparing the fault classification probability with a preset confidence threshold to generate a fault diagnosis result of the mutual inductor. By implementing the method, the recognition sensitivity of the system on early weak faults and high-frequency distribution parameter changes of the mutual inductor can be improved.