Power transformer fault diagnosis method based on stacked time series network

Patent metadata

Field	Value
Publication number	US-2024037387-A1
Application number	US-202218060583-A
Country	US
Kind code	A1
Filing date	Dec 1, 2022
Priority date	Jul 26, 2022
Publication date	Feb 1, 2024
Grant date	—

Abstract

A power transformer fault diagnosis method based on a stacked time series network, includes: collecting gas-in-oil data of a transformer in each substation; performing z-score normalization on the collected data to obtain a normalized matrix; dividing the normalized matrix into a training set and a test set in proportion; constructing a stacked time series network based on Xgboost and a bidirectional gated neural network, and inputting the training set and the test set to perform network training; and normalizing real-time collected data to obtain trainable data to predict a fault and update network parameters. The gas-in-oil data is predicted by using Xgboost and a gated neural network, obtains prediction data of a power transformer from two time series networks by using a meta learner, and obtains a fault diagnosis result of the transformer by using a Softmax layer. The neural network has accurate fault diagnosis performance and stable robustness.

First claim

What is claimed is:

1 . A power transformer fault diagnosis method based on a stacked time series network, comprising:
(1) collecting gas-in-oil information of each substation, wherein the gas-in-oil information comprises monitoring information of an oil test and contents of dissolved gas and furan in oil in each substation; (2) normalizing the collected gas-in-oil information to obtain a normalized matrix; (3) dividing the normalized matrix into a training set and a test set in proportion to train network parameters; (4) constructing a stacked time series network based on Xgboost and a bidirectional gated neural network, inputting the training set and the test set to train the stacked time series network, and learning a feature of gas-in-oil data of a transformer; and (5) performing fault diagnosis based on real-time gas-in-oil data during operation, and fine tuning a weight of the stacked time series network to enable the stacked time series network to continuously learn a new feature.

2 . The method according to claim 1 , wherein the gas-in-oil information comprises data of the transformer during operation and data recorded by an electric power company, and each group of data comprises gas-in-oil data and a fault state of the corresponding transformer, wherein the gas-in-oil data comprises contents of nine key states: a breakdown voltage (BDV), water, acidity, hydrogen, methane, ethane, ethylene, acetylene, and furan.

3 . The method according to claim 1 , wherein step (2) comprises: performing z-score normalization on the gas-in-oil information to obtain the normalized matrix.

4 . The method according to claim 1 , wherein the data in the gas-in-oil information is divided into two parts in step (3), wherein data of a certain proportion is used as the training set to train the stacked time series network, and a data of a remaining proportion is used as the test set to test a fault diagnosis effect of the stacked time series network for the transformer.

5 . The method according to claim 4 , wherein step (4) comprises:
(4.1) constructing the stacked time series network based on Xgboost and the bidirectional gated neural network to perform feature extraction and prediction on the gas-in-oil information, wherein construction of Xgboost comprises establishment of an integrated model, selection of an objective function, and solving of a loss function; and the bidirectional gated neural network comprises a forward calculation layer, a backward calculation layer, an update gate, and a reset gate; (4.2) predicting gas in the oil by using Xgboost and the bidirectional gated neural network, and outputting a prediction result of the gas-in-oil information; and (4.3) training prediction results of Xgboost and the bidirectional gated neural network by using a meta learner, to output the prediction result of the gas-in-oil information, performing fault diagnosis on the stacked time series network by using a Softmax layer, and outputting the fault state of the transformer.

6 . The method according to claim 5 , wherein the construction of Xgboost comprises the establishment of the integrated model, the selection of the objective function, and the solving of the loss function, wherein the establishment of the integrated model is to recursively construct a binary decision tree, and in input space of the training set, each region is recursively divided into two sub-regions based on a minimum squared-error criterion, and an output value of each sub-region is determined; the selection of the objective function is to measure an error between a predicted value and a real value of a target, and the objective function is approximated through second-order Taylor expansion; and the solving of the loss function is to partition a sub-tree by using a greedy algorithm, enumerate feasible partitioning points, in other words, add a new partition to an existing leaf each time, and calculate a corresponding maximum gain.

7 . The method according to claim 6 , wherein the bidirectional gated neural network comprises the forward calculation layer, the backward calculation layer, the update gate, and the reset gate, wherein the reset gate helps to capture a short-term dependency in a time series, the update gate helps to capture a long-term dependency in the time series, and the forward calculation layer and the backward calculation layer process the input series in turn.

8 . The method according to claim 7 , wherein the meta learner trains and predicts the results of Xgboost and the bidirectional gated neural network, and the meta learner is constructed as a linear regression model to learn and predict the results of Xgboost and the bidirectional gated neural network.

9 . The method according to claim 8 , wherein step (5) comprises:
performing z-score normalization on real-time collected gas-in-oil data, and then dividing normalized data into the training set and the test set to train the stacked time series network for fault diagnosis, wherein if a new data type or a relevant influencing factor needs to be added, the original stacked time series network is taken as a pre-training model to activate all layers for training.

Assignees

• Univ Wuhan
• State Grid Tianjin Electric Power Co

Inventors

• He Yigang
• Xing Zhikai
• Wang Xiao
• Liu Xiaoyu
• Jiang Xue
• Gong Qingwu
• Wang Jianfeng
• Ma Shiqian

Classifications

• Y04S10/50

  Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications · CPC title

• G06N3/08 Primary

  Learning methods · CPC title

• G05B23/0283

  Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] (preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06) · CPC title

• G01N33/2841 Primary

  Gas in oils, e.g. hydrogen in insulating oils · CPC title

• G06N3/049

  Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs · CPC title