1142results about "Transformers testing" patented technology

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

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

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

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

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

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

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

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

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