11 results about "Dissolved gas analysis" patented technology

The invention provides a power frequency arc intelligent diagnosis method, device and equipment and a medium, and relates to the technical field of power frequency arc diagnos.The method comprises the steps that optical signal data and analysis data of gas dissolved in oil are obtained; the optical signal data and the dissolved gas analysis data are preprocessed, and a training data set is constructed based on the preprocessed data and the corresponding manual distribution labels; the training data set and a preset target loss function are adopted to train a double-flow neural network model, a target fault diagnosis model is obtained, and the double-flow neural network model comprises an optical signal network branch, an analysis data network branch and a fusion module; and inputting the real-time monitoring data into the target fault diagnosis model to obtain a target fault diagnosis result. Through the intelligent diagnosis method for the power frequency arc, early warning and accurate fault type diagnosis of the power frequency arc can be realized.

The application discloses a transformer fault diagnosis method based on a small sample unbalanced data set, comprising the following steps: S1, acquiring a transformer dissolved gas analysis data set and a comprehensive feature set; S2, constructing a transformer fault diagnosis model based on a small sample unbalanced data set; S3, according to the gas analysis data set and the comprehensive feature set, performing model training on the constructed transformer fault diagnosis model to obtain an optimal fault diagnosis model; and realizing transformer fault diagnosis based on the small sample unbalanced data set according to the optimal fault diagnosis model. The application solves the problems that the current traditional method cannot accurately diagnose transformer faults due to the technical problems such as complex fault mode recognition, specific class accurate diagnosis, insufficient model generalization ability and difficulty in fusion parameter optimization under a small sample unbalanced fault data set in the prior art.

The invention discloses a quantitative analysis method and system for overheat fault gas of an on-load tap-changer contact. The method comprises the following steps: constructing a multi-physical field coupling mathematical model of an on-load tap-changer, and defining contact resistance of a contact surface; solving the multi-physics field coupling model to obtain time domain and space domain temperature distribution of the surface of the on-load tap-changer contact, and determining the highest temperature and the volume of an overheat region; calculating the local gas production rate of each fault characteristic gas; carrying out integral calculation on local gas production rates of all fault characteristic gases in the hot area volume to obtain theoretical equilibrium concentration of each fault characteristic gas in oil, and constructing a mapping relation between an overheating parameter formed by the contact resistance and the highest temperature of the contact surface and the theoretical equilibrium concentration; the method aims at solving the problems that a traditional analysis method for the gas dissolved in the oil depends on experience criteria and is high in hysteresis quality, breaks through the bottleneck that the whole process of electricity-heat-fluidization cannot be simulated from the mechanism level, and achieves the crossing from qualitative diagnosis to quantitative prediction.

The application discloses a kind of overheat fault gas quantitative analysis method and system of on-load tap-changer contact, the method of the present application includes constructing the multi-physics field coupling mathematical model of on-load tap-changer, define contact surface contact resistance;Solving multi-physics field coupling model obtains the time domain and spatial temperature distribution of on-load tap-changer contact surface, determine the highest temperature and the volume of overheated area;Calculate the local gas production rate of each fault characteristic gas;The local gas production rate of all fault characteristic gases in the volume of hot area is integrated to obtain the theoretical equilibrium concentration of each fault characteristic gas in oil, construct the mapping relationship between the overheating parameters of contact surface contact resistance and highest temperature and theoretical equilibrium concentration.The present application aims to solve the problem that the traditional oil dissolved gas analysis method relies on empirical criterion and has high hysteresis, break through the bottleneck that cannot simulate the whole process of "electric-thermal-flow-chemical" from mechanism level, realize the leap from qualitative diagnosis to quantitative prediction.

The invention discloses a transformer service characteristic evaluation system fused with a BiLSTM network, and the system comprises a multi-source data collection module which is used for collecting grounding resistance dynamic monitoring data, analysis data of gas dissolved in oil, vibration spectrum data, and infrared thermal imaging data of a transformer; the data preprocessing module is in communication connection with the multi-source data acquisition module and is used for performing space-time alignment, noise filtering and feature standardization processing on the acquired multi-source data; the invention relates to the technical field of power systems. According to the transformer service characteristic evaluation system fused with the BiLSTM network, grounding resistance, gas dissolved in oil, vibration spectrum and infrared thermal imaging data are integrated through the multi-source data acquisition module, and each acquisition unit has high precision, so that the one-sidedness of traditional single-source data acquisition can be avoided; more complete and accurate original data support is provided for transformer service characteristic evaluation, and evaluation deviation caused by data missing or insufficient precision is reduced.

The invention discloses a transformer fault diagnosis method based on a small sample unbalanced data set. The method comprises the following steps: S1, acquiring an analysis data set and a comprehensive feature set of gas dissolved in a transformer; s2, constructing a transformer fault diagnosis model based on the small sample unbalanced data set, and S3, performing model training on the constructed transformer fault diagnosis model according to the gas analysis data set and the comprehensive feature set to obtain an optimal fault diagnosis model; and transformer fault diagnosis based on the small sample unbalanced data set is realized according to the optimal fault diagnosis model. According to the method, the problem that the transformer fault cannot be accurately diagnosed due to the technical problems of complex fault mode recognition, specific category accurate diagnosis, insufficient model generalization ability, difficulty in fusion parameter optimization and the like in the condition of adapting to a small sample unbalanced fault data set in the prior art is solved.

The invention discloses a transformer state signal analysis method, and relates to the technical field of transformers, and the method comprises the steps: converting dissolved gas analysis data into physical characteristic data based on a preset prior knowledge base; judging a current sample mode of the target transformer according to the physical characteristic data; if the current sample mode is a known mode, inputting the physical characteristic data into a pre-trained multivariate classifier to obtain a basic fault physical mode label of the target transformer; if the current sample mode is an unknown mode, inputting the physical feature data and the equipment parameters into a pre-trained semantic mapping model to obtain distribution representation data in a semantic space, the distribution representation data being a probability distribution parameter set; determining the negative logarithm likelihood of the distribution representation data and the plurality of composite fault prototypes; and determining a composite fault physical mode label of the target transformer according to the composite fault prototype corresponding to the minimum one of the plurality of negative logarithm likelihoods. According to the invention, the accuracy of transformer oil state monitoring is improved.

The invention relates to a microfluidic device for analyzing dissolved gas in insulating oil and a control method. The micro-fluidic device comprises a micro-fluidic chip and a heating device, the micro-fluidic chip is provided with an oil sample inlet, a micro-channel area and a gas-liquid separation area, the oil sample inlet is communicated with the micro-channel area, the micro-channel area is communicated with the gas-liquid separation area, a pump cavity used for containing fluid is formed in the micro-channel area, and the heating device is arranged in the pump cavity. The pump cavity is respectively communicated with the oil sample inlet and the gas-liquid separation area, a piezoelectric brake is arranged in the pump cavity, the piezoelectric brake is connected with an elastic pump membrane of the pump cavity, and the heating device is used for heating the micro-channel area; the piezoelectric brake and the heating device are integrated on the micro-fluidic chip, and the micro-fluidic chip is small in size and high in extraction efficiency.

The invention provides a chromatographic detection system and detection method for analyzing dissolved gas in transformer oil, and the system comprises an integrated solenoid valve type valve terminal module which is used for carrying out flow path switching and controlling a sample to enter an integrated constant flow separation module; the integrated constant-current separation module comprises an input module for a constant-current sample, a first chromatographic column channel for separating a first gas in the sample and a second chromatographic column channel for separating a second gas in the sample; the composite detection module comprises a first catalytic combustion sensor arranged at the outlet of the first chromatographic column channel, and an infrared optical sensor and a second catalytic combustion sensor which are arranged at the outlet of the second chromatographic column channel and are connected in series; and the back-blowing stable base line module is connected with a back-blowing gas circuit controlled by the integrated electromagnetic valve type valve terminal module, and is used for back-blowing the double chromatographic column to remove residual components. On the basis of integration of electromagnetic valve terminal control, double-column parallel separation and multi-sensor detection, the high-standard requirement for online monitoring of the state of power equipment is met.

The present invention provides a transformer malfunction diagnostic device and a malfunction diagnosis method using same, wherein a rule-based learning method is combined with a deep-learning-based learning method based on artificial intelligence. The transformer malfunction diagnosis device according to an embodiment of the present invention comprises: a data scaling unit for scaling dissolved gas analysis data acquired from a transformer, a provisionally labeled data acquisition unit for converting unlabeled data, among the scaled dissolved gas analysis data, to provisionally labeled data and acquiring same; a prelearning unit for performing prelearning for the unlabeled data, among the scaled dissolved gas analysis data, and the provisionally labeled data; and a relearning unit for performing relearning for a labeled data through the transformer of parameters optimized by performing the prelearning.

The invention discloses a transformer fault diagnosis method, and relates to the field of fault diagnosis, and the method comprises the steps: obtaining a dissolved gas analysis data set of a transformer, carrying out the optimization of the dissolved gas analysis data set through employing a factor analysis method, and obtaining a dissolved gas analysis optimization data set; inputting the dissolved gas analysis optimization data set into the trained fault diagnosis model to obtain a transformer fault diagnosis result; the fault diagnosis model comprises a feature precoding module, a TransformerFat module, an attention module and a trans-attention fusion module; the feature precoding module is used for obtaining a time sequence feature vector; the TransformerFeat module is used for carrying out feature extraction on the time sequence feature vector to obtain a global feature; the attention module is used for performing feature extraction on the global features to obtain local features; and the trans-attention fusion module is used for fusing the global features and the local features to obtain a transformer fault diagnosis result. According to the invention, the accuracy and robustness of transformer fault diagnosis are improved.