455results about "Transformers testing" patented technology

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

The invention relates to the technical field of control systems, and particularly discloses an intelligent control system for an insulation test of a marine dry-type transformer. The system comprises a high-voltage test power supply module, a multi-mode sensing acquisition module, an insulation state evaluation module, a self-adaptive boost decision module and a test process control module, and evaluates an insulation state and dynamically decides a boost rate and a holding strategy by acquiring voltage, leakage current, partial discharge and temperature data in real time. Intelligent control and safety protection in the insulation test process are achieved, potential insulation damage is effectively avoided, and diagnosis precision is improved.

The invention relates to a fault detection method and device of a converter transformer, computer equipment and a medium. The method comprises the steps of obtaining a vibration time-domain waveform signal and a current time-domain waveform signal of a winding of the converter transformer in a preset time period, determining vibration frequency response data of the winding according to the vibration time-domain waveform signal and the current time-domain waveform signal, and determining the current time-domain waveform signal of the winding according to the vibration frequency response data and preset reference vibration frequency response data. And determining a fault detection result of the winding. By adopting the method, the timeliness of fault detection of the converter transformer can be improved.

The invention relates to the technical field of power equipment state detection, in particular to a transformer winding state detection method, system and equipment based on dynamic frequency band adaptation and a storage medium. Collecting full-band frequency response data of the healthy reference winding, and performing adaptive adjustment on a preset basic frequency band based on the peak-valley distribution density to obtain a reference target frequency band matched with the frequency spectrum characteristics of the winding; generating a reference polar coordinate feature map through a phase-amplitude mapping rule, and extracting pattern texture features to construct a reference feature library; collecting detected winding data and taking the reference target frequency band as a detection frequency band to generate a detected polar coordinate feature map; a multi-dimensional evaluation result is obtained through graph space similarity comparison and feature difference quantification in combination with amplitude anomaly recognition; and outputting a normal, slight fault or serious fault state judgment result of the tested winding based on the preset evaluation threshold interval and the working condition adaptation correction rule. The problems that in the prior art, frequency band division is fixed, and threshold values lack working condition adaptation are solved.

The invention provides a transformer abnormity monitoring method and system based on artificial intelligence, relates to the technical field of transformer state monitoring, and solves the technical problems of poor scene adaptability, multi-source data isolation and weak early warning in the prior art. The method comprises the following steps: acquiring real-time data and historical data of a transformer; preprocessing the real-time data through a preprocessing algorithm to obtain standard data; constructing an AI anomaly monitoring model based on historical data; wherein the AI anomaly monitoring model comprises a dynamic baseline module, an anomaly detection module and an anomaly classification module; and inputting the standard data into the AI anomaly monitoring model to generate an anomaly alarm. The method is used in the transformer abnormity monitoring process.

The invention relates to the technical field of automation and intelligent monitoring of a power system, and discloses an intelligent monitoring method for a transformer substation, which comprises the following steps: acquiring historical operation data of a transformer, real-time meteorological data of the transformer substation and future task planning information; performing time alignment and missing value interpolation, and constructing a multi-dimensional time sequence input matrix; inputting the data into an insulation aging state coupling evaluation model based on a physical information neural network to generate an insulation aging state index; and triggering graded early warning according to a comparison result of the index and a preset threshold value. According to the model, a heat-electricity-humidity multi-physics field coupling equation and the attention enhancement LSTM are fused, and high-precision and low-false-alarm online monitoring is achieved. The false alarm rate is obviously reduced, and the operation and maintenance safety and economical efficiency of the transformer substation are improved.

The invention provides a transformer internal partial discharge detection method, device and equipment and a storage medium, and belongs to the field of transformer monitoring, and the method comprises the steps: obtaining an ultrahigh frequency electric signal, an optical signal and an acoustic signal generated by transformer internal partial discharge through an ultrahigh frequency sensor, an optical sensor and an optical fiber Fabry-Perot ultrasonic sensor; a multi-state quantity integrated sensing SoC chip on the circuit board is used for conditioning and digitizing the signals to generate a digitized signal flow; inputting the signal flow into a state sensing special edge computing NPU chip, processing the signal flow by using a graph attention network algorithm, calculating an attention coefficient between sensor nodes, and fusing the multi-modal signals to generate a fusion signal; and determining the partial discharge type according to the fusion signal. According to the invention, three sensors are internally integrated, signal conditioning digitalization is carried out by using the SoC chip, multi-mode signals are fused through the NPU chip, high-precision and high-reliability identification is realized, and the difficulty of an external sensor is overcome.

The invention belongs to the technical field of signal detection, and discloses an arrangement optimization method of detection coils for transformer turn-to-turn short circuit fault detection, which comprises the step of uniformly arranging five detection coils in a high-low voltage winding gap corresponding to each coil section along the axial direction so as to ensure efficient detection and accurate positioning of a transformer turn-to-turn short circuit fault. The method is not a pure data black box, but intelligently fuses a physical mechanism and data driving, and ensures that an optimization scheme not only conforms to an electromagnetic physical rule, but also has data self-adaptive capability. The PNN supports end-to-end learning, features can be directly extracted from original signals, the defects of traditional feature engineering are overcome, and the determination efficiency and objectivity of an optimization scheme are greatly improved. Layout optimization and accurate positioning are realized synchronously: in a primary optimization process, the algorithm can accurately position a fault wire turn while outputting an optimal sensor layout scheme.

The application discloses a kind of key feature transformer health state detection systems, it is related to power equipment monitoring technical field, including: data acquisition module, data processing module, confidence calculation module, fault determination module and result output module;Data acquisition module synchronously acquires transformer oil tank surface vibration signal and neutral point direct current data;Data processing module calculates the phase difference of vibration signal and the coherence coefficient of vibration signal and direct current data;Confidence calculation module calculates mechanical fault confidence;Fault determination module judges whether mechanical fault confidence exceeds preset threshold, exceeds and then carries out winding loosening positioning processing, generates loosening coordinate;Otherwise, the multi-band resampling of vibration signal is carried out and the coherence coefficient data is recalculated;Result output module outputs loosening coordinate or interference warning signal;The application can improve the accuracy of transformer health state detection, ensure the reliability of fault positioning.

The invention discloses a transformer winding vibration abnormity diagnosis and early warning method based on lightweight edge calculation, and relates to the technical field of transformer monitoring. According to the method, vibration signals and magnetic flux signals of a transformer winding are collected in real time at edge nodes, and a basic data set is obtained after preprocessing; vibration and magnetic flux features are extracted to construct a basic diagnosis feature vector, a three-dimensional tensor is constructed through directional coherence analysis, a boundary enhancement structure map is generated, and a structure enhancement fusion feature vector is formed after fusion. The method comprises the following steps of: realizing abnormal grade and trend prediction by a lightweight Transform diagnosis model based on knowledge distillation and parameter compression optimization, and triggering model adaptive updating by combining risk index modeling and a dynamic risk threshold value. And finally, calculating an abnormal level posterior probability through a Bayesian updating rule, and outputting multi-level early warning. The method has the advantages of high precision, low calculation overhead and self-adaptive capability, and is suitable for online monitoring and intelligent operation and maintenance of the state of the transformer.

A method for determining faults on a power supply network is disclosed, including determining which consumers are supplied by a particular substation. If a consumer is selected and the consumer's substation data is incomplete, a set of supply quality information is obtained for the consumer's facilities. This process is repeated for a number of other consumers, and the data for each of them is compared with the data for the first consumer. Once the subnetwork is correctly characterized, further supply quality information is analyzed to provide warnings about network problems, provide short-term and medium-term fixes for such problems, and also provide long-term solutions, including network redesign.

A pumped storage unit main transformer winding inter-turn short circuit fault diagnosis positioning method and system, comprising: pulse injection is carried out on the main transformer of the pumped storage unit in the shutdown state, and the voltage response waveform is obtained; the characteristic waveform is calculated, the different connection modes of the pumped storage unit main transformer winding are combined, whether the pumped storage unit main transformer winding exists fault and the fault phase are judged; the VMD decomposition optimization model and the NTEO wave head extraction optimization model with optimal parameters are constructed, and the instantaneous energy spectrum is obtained based on the model and any characteristic phase containing characteristic phase; the fault positioning formula is constructed based on the instantaneous energy spectrum, the fault positioning formula corresponding to the relationship between the characteristic phase and the fault phase in the voltage response waveform is selected according to the relationship between the characteristic phase and the fault phase, and the positioning of the fault coil is obtained. The method has high reliability, high fault diagnosis accuracy and small positioning result error, and can meet the needs of pumped storage unit main transformer winding inter-turn short circuit fault diagnosis and positioning in engineering.

This invention provides a method and system for identifying and detecting harmonic features of core grounding current in distribution transformers, relating to the field of transformer technology. The method includes acquiring the current signal of the core grounding circuit, performing time-frequency domain transformation, and extracting harmonic feature vectors; constructing a harmonic coupling constraint relationship model based on the nonlinear saturation characteristics of core magnetization to identify characteristic harmonic combinations; establishing a mapping rule between the harmonic amplitude ratio sequence and the grounding state type to calculate the matching degree; and outputting an abnormal result and updating the model when the matching degree exceeds a preset criterion. This invention can accurately identify core grounding faults, improving detection accuracy and reliability.

The invention relates to a power transformer on-line monitoring method, system and device and a medium. An existing power transformer on-line monitoring method cannot carry out self-adaptive adjustment on an operation environment. The method comprises the following steps: acquiring operation data and environment data of a transformer in real time; according to the operation data and the environment data, a corresponding early warning threshold is matched according to a preset threshold adjustment rule; obtaining one or more of the current transformer temperature, the current sudden change rate or the current vibration parameter as a target parameter; calculating the real-time deviation degree of the target parameter relative to the early warning threshold value, and monitoring the change rate of the target parameter; and when the deviation degree and the change rate of the target parameter meet a preset condition, generating a risk early warning signal of a corresponding level. The operation data and the environment data of the transformer are collected in real time, the early warning threshold value is dynamically adjusted according to the preset threshold value adjusting rule and the operation data and the environment data, and the early warning threshold value can adapt to different operation environments and load conditions.

The invention relates to the technical field of locomotive line detection, in particular to a line detection system and device for a locomotive main transformer, and the system comprises a transformer which comprises an input side and an output side, the input side is used for being connected with a power supply, the output side comprises an end a and an end n, the end a is used for being connected with the end A of the main transformer, and the end n is used for being connected with the end X of the main transformer; the end a is connected with an over-current relay; the input side is connected with a conduction test loop, an operation test loop and an overload test loop; the conduction test loop is used for testing the conduction states of the main transformer, the a end and the n end; the operation test loop is used for carrying out power supply detection on the main transformer; when the transformer is in overload operation, the overcurrent relay drives the overload test loop to be conducted; therefore, through the arrangement of the conduction test loop, the operation test loop and the overload test loop, the conduction state, the power supply condition and the overload condition of the main transformer line can be rapidly detected, and cumbersome connection and disconnection operation is not needed.

The present application provides a method for monitoring a through fault current. The method may involve: detecting a through fault; calculating an electrical stress, peak current, and duration of the through fault; determining two sets of percentage state changes associated with the through fault: assigning a respective set of weights and criticalities to each set of percentage state changes; calculating a mechanical state change based on the first set of percentage state changes and the first set of weights and criticalities; calculating a thermal state change based on the second set of percentage state changes and the second set of weights and criticalities; calculating a cumulative state change based on the mechanical and thermal state changes; and training a machine learning model using the electrical stress, peak current, duration, two sets of percentage state changes, and mechanical, thermal, and cumulative state changes.

The invention discloses a transformer evaluation method and system combining multi-physics field inversion and a graph neural network, and the method comprises the steps: obtaining the external monitoring data of a transformer, and building a multi-dimensional time sequence based on the external monitoring data; obtaining internal response data corresponding to the multi-dimensional time sequence through a multi-physical field rapid calculation model; establishing a mapping relationship between the internal monitoring quantity of the transformer and the internal response data, and correcting parameters of the multi-physical field rapid calculation model based on the determined target function and the mapping relationship; obtaining corrected internal response data corresponding to the multi-dimensional time sequence based on the corrected multi-physical field rapid calculation model; and evaluating the state of the transformer through a state evaluation model based on the external monitoring data and the corrected internal response data to obtain an evaluation conclusion.

Disclosed are a method and system for detecting the short-circuit withstand capability of a transformer, a terminal, a medium, and a program. The method comprises: acquiring transformer design parameters and operating conditions of a power system in which the transformer is located; on the basis of the transformer design parameters and the operating conditions of the power system in which the transformer is located, obtaining a maximum short-circuit through current value of a transformer winding; inputting the maximum short-circuit through current value of the transformer winding into a first preset calculation model, and obtaining a first stress value of each coil cake of the transformer winding; using a second preset mode to obtain a second stress value of each coil cake of the transformer winding, then using a third preset mode to obtain a second current value of the transformer winding; and finally obtaining a safety margin of the transformer to determine the short-circuit withstand capability of the transformer. According to the present invention, even when radial support is invalid, accurate detection of the short-circuit withstand capability of a transformer can still be implemented.

The application discloses a transformer short-circuit winding amplitude force failure test model equivalent method and device. The application determines a winding prototype and a winding prototype specification according to a transformer to be tested, establishes a winding equivalent model in combination with the winding prototype and the winding prototype specification, carries out an amplitude force failure test on the winding equivalent model based on a pre-defined amplitude force failure mode, obtains a first amplitude force failure characteristic value, carries out an amplitude force failure test on the transformer to be tested based on the amplitude force failure mode, obtains a second amplitude force failure characteristic value, and judges whether the winding equivalent model is used as a winding amplitude force failure test model according to the first amplitude force failure characteristic value and the second amplitude force failure characteristic value. The winding amplitude force failure test model can be established for the transformer to be tested, which is beneficial to accurately reflecting the winding amplitude force failure condition of the transformer to be tested and guaranteeing the equivalence and correctness of the winding amplitude force stability research.

The invention discloses a double-domain BBO transformer coupling fault diagnosis method based on a habitat transformation mechanism, and the method comprises the steps: constructing a chemical domain population and an electromagnetic domain population based on the operation feature data of a transformer; constructing a fault coupling matrix; fitness values of habitats in the current chemical domain population and the electromagnetic domain population are calculated, and migration operation of the standard BBO is executed based on the fitness values; in each iteration of executing the basic migration operation, when an elitist solution meeting a preset condition exists in the electromagnetic domain population, performing habitat transformation on the chemical domain population according to the elitist solution and the fault coupling matrix, including search space distortion and feature dimension dynamic reconstruction; and calculating fitness values of the current electromagnetic domain population and the chemical domain population, and determining the fault type of the transformer through weighted fusion. According to the method, the transformer to be detected does not need to be subjected to power failure hanging cover detection, high-precision state evaluation can be realized only by using online monitoring data, the operation and maintenance cost can be saved, and the method has extremely high engineering practical value.

The present application relates to the technical field of transformer evaluation, and particularly relates to a light-weight medium-high frequency transformer conversion efficiency evaluation method and system, which comprises the following steps: obtaining structure winding parameters, light-weight parameters and operation condition parameters of a transformer to be evaluated, and constructing an evaluation data set; constructing an efficiency evaluation model, which at least comprises an iron loss sub-model and a copper loss sub-model, and setting model parameters of the iron loss sub-model and the copper loss sub-model as to-be-identified parameters; obtaining test data corresponding to the transformer to be evaluated, and performing parameter calibration on the to-be-identified parameters; inputting the evaluation data set into the calibrated efficiency evaluation model, outputting loss decomposition results and efficiency results of each working condition point, and obtaining conversion efficiency evaluation results. Through the present application, the problem that the efficiency evaluation of a light-weight medium-high frequency transformer in the prior art relies on experience or single-point testing and is difficult to model and calibrate in multiple working conditions, thereby leading to large deviation, is effectively solved, and accurate evaluation of multiple working condition loss decomposition and conversion efficiency is realized.

The application provides a transformer fault diagnosis method and device based on a multi-modal self-attention mechanism, and the method comprises the following steps: collecting multi-modal monitoring data of a transformer to obtain a data sequence matrix of multiple modes; pre-processing the matrix to obtain pre-processed data; calculating the change rate and change point index to obtain a change rate and change point feature set; performing time sequence attention calculation on each mode based on the pre-processed data and the feature set to obtain single-mode feature representation; performing cross-modal attention interaction calculation based on the single-mode feature representation to obtain global cross-modal fusion feature representation; fusing the single-mode feature representation and the global cross-modal fusion feature representation to obtain fusion representation; and inputting the fusion representation into a pre-trained classifier to obtain a transformer fault diagnosis result. The application realizes reliable mutual verification of multi-modal information by extracting dynamic feature indexes and performing multi-modal self-attention mechanism processing, and improves the transformer fault diagnosis accuracy and early warning capability.

The utility model discloses an anti-interference shielding case for deformation detection of a transformer winding, which relates to the technical field of transformer detection equipment and comprises a shielding case main body and a movable door slidably arranged in the shielding case main body. The shielding cover main body, the movable door and the top plate are arranged to form a shielding structure for wrapping the transformer winding, electromagnetic interference of the external environment on the transformer winding can be isolated in the detection process of the transformer winding, the heat conduction copper pipes are arranged in the middle of the top plate, and the heat conduction copper pipes are provided with the horizontal parts and the inclined parts, so that the heat conduction efficiency is improved. The horizontal part can achieve heat exchange with heat in air in the shielding structure through the heat conduction fins on the outer side of the horizontal part, and the inclined part can achieve heat exchange between heat in the heat conduction copper pipe and outside air through the heat dissipation fins on the outer side of the inclined part. And the transformer winding is prevented from being damaged by heat accumulation while the anti-electromagnetic interference on the transformer winding is realized.

The present application provides a method for monitoring a through fault current. The method may involve: detecting a through fault; calculating an electrical stress, peak current, and duration of the through fault; determining two sets of percentage state changes associated with the through fault; assigning a respective set of weights and criticalities to each set of percentage state changes; calculating a mechanical state change based on the first set of percentage state changes and the first set of weights and criticalities; calculating a thermal state change based on the second set of percentage state changes and the second set of weights and criticalities; calculating a cumulative state change based on the mechanical and thermal state changes; and training a machine learning model using the electrical stress, peak current, duration, two sets of percentage state changes, and mechanical, thermal, and cumulative state changes.

The invention relates to a method, in particular a computer-assisted method, for monitoring the state of an electrical device, preferably for monitoring the short-circuit current rating of an electrical device, in particular a transformer (1) or a choke, preferably a compensation choke, wherein the electrical device comprises a solid insulation assembly (I) and a liquid and / or gaseous insulation medium which is in contact with the solid insulation assembly (I), the method having the following steps: S1) measuring operating parameters of the electrical device; S2) providing and / or creating a simulation model of the electrical device; S3) carrying out a simulation for the electrical device using the simulation model and the measured operating parameters; and S4) evaluating the state of the electrical device, in particular the short-circuit current rating of the electrical device, on the basis of the results of the simulation.

Test arrangement, which includes test components interconnected via a data bus safer, and method of operating the test arrangement. Test components of the test devices are connected according to a specified test configuration; at least one test device having a functional-test unit is defined as an initiator device; all test components connected to the data bus are determined and stored as a test-component list in the initiator device; the test-component list is transmitted to a verification unit connected to the initiator device and displayed; the displayed test-component list is checked; the displayed test-component list is confirmed when it corresponds to the specified test configuration, and the test-component list is transmitted as a user configuration list to the initiator device and compared with the test-component list; and the test arrangement is brought into a ready-for-operation state when the user configuration list received corresponds to the stored test-component list.

The invention relates to a method, in particular a computer-aided method, for monitoring the moisture of a fixed insulation arrangement (I) and / or a liquid and / or gaseous insulating medium of an electrical device (1), in particular a transformer (1) or a choke, which is in contact with a fixed insulation arrangement (I), wherein the method comprises the following steps: S1) a simulation model of the electrical device is provided and / or created; S2) a simulation of the moisture distribution in the electrical device (1), in particular in the fixed insulation arrangement (I) and / or in the insulating medium, is carried out using the simulation model.

The application belongs to the field of power system fault diagnosis, and provides a three-phase power transformer inter-turn fault detection method based on equivalent magnetic circuit to solve the technical problem that it is difficult to identify inter-turn fault through each phase working voltage. The equivalent magnetic circuit under normal operation state is established according to the structural characteristics of the three-phase power transformer, and is simplified. Any phase in the equivalent magnetic circuit under normal operation state after simplification is selected as a fault phase, and the equivalent magnetic circuit under inter-turn fault state is established. The equivalent magnetic circuit under inter-turn fault state is analyzed, and the following conclusion is derived: only when inter-turn fault occurs, N f I f ≠ 0, wherein N f represents the number of turns shortened on the primary or secondary winding due to inter-turn fault, i.e. fault turn number; I f represents the induced current on the fault turn number, i.e. fault current; whether inter-turn fault occurs is judged according to N f I f . The application has economy, and can stably detect low-level inter-turn fault during system transient and external fault because the phase voltage is decoupled.

The application relates to the technical field of transformer fault diagnosis, and discloses a TFD system and method based on spatiotemporal feature fusion and explainability enhancement. First, multi-source time series data selection and dynamic spatiotemporal feature construction are performed; then, multi-modal spatiotemporal features are extracted by adopting a double-channel, the double-channel output features are spliced, a cross-modal attention mechanism is introduced, the fusion weights of four categories of features are automatically calculated, and a weighted unified feature representation is generated; a bidirectional long short-term memory network is adopted to model the fused unified feature representation, capture the forward and backward time series dependence relationship of fault development, and output time series related feature encoding; finally, an explainable reasoning and output module is used to generate an explainable diagnosis report conforming to industry specifications and provide decision basis visualization. The application enhances early warning sensitivity, improves the comprehensiveness and context perception ability of fault diagnosis, and provides intuitive understanding model judgment basis for operation and maintenance personnel.