Wuhan University

State Grid Tianjin Electric Power Company

Yigang He

Liulu He

Hui Zhang

Jiajun Duan

Chaolong Zhang

Lie Li

Bolun Du

Guolong Shi

Baiqiang Yin

Bing Li

Xiaoxin Wu

Chenyuan Wang

Mingyun Chen

Yaru Zhang

Kaipei Liu

Yongbo Sui

Xiaoyu Liu

Zhaorong Zeng

Chuankun Wang

Weibo Yuan

Yuan Huang

Yingying Zhao

Xiao Wang

Lei Wang

Ming Xiang

Yuzheng Guo

Hui Shao

Yuan Chen

Zhong Li

Weiwei Zhang

Chunsong Sui

Wei He

Tao Wang

Shiqian Ma

Xiaoyan Liu

Jianbo Zhou

Lei Xu

Zihao Li

Wenjie Wu

Luqiang Shi

Shuiqing Xu

Yuting Wu

Lei Zuo

Shudong Wang

Jinguang Jiang

Jin Tong

Jian Zheng

Qiwu Luo

Tongtong Cheng

Kaiwei Li

Shuguang Ning

Yuanxin Xiong

Yongguang Cheng

Yi Ruan

Jie Xie

Zhikai Xing

Jintao Zhou

Liufei Shen

Wei Wu

Jianfeng Wang

Yuanzhe Ge

Qingwu Gong

Qizhen Li

A method for diagnosing transformer fault based on a deep coupled dense convolutional neural network, includes: obtaining datasets of dissolved gas in oil of a transformer in normal and fault states; expanding the datasets by using an adaptive synthetic oversampling method; performing, in a form of a two-dimensional matrix, feature reconstruction on characteristic gas dissolved in the oil; building a transformer fault diagnosis model based on a deep coupled dense convolutional neural network; and dividing an expanded dataset into a training set and a test set, and taking the two-dimensional matrix as an input of the deep coupled dense convolutional neural network and a set label as an output to train the network to obtain a fault diagnosis model. The present disclosure can resolve a problem that a fault diagnosis accuracy rate of the transformer is low due to insufficient and unbalanced fault samples in the dissolved gas in the oil.

Method for diagnosing transformer fault based on deep coupled dense convolutional neural network

The disclosure discloses a single-ended fault positioning method and system for a HVDC power transmission line based on a hybrid deep network. The method comprises the following: collecting rectification side bus output voltage and current signals of a HVDC power transmission system under different fault types, fault distances and transition resistances as an original data set; eliminating electromagnetic coupling of the bipolar direct-current line by using phase-mode transformation, extracting IMF components of fault voltage and current signals under different fault scenes by using variational mode decomposition, and calculating TEO of the IMF components to obtain a fault data set after feature engineering; normalizing the fault data set, and dividing the fault data set into a training set and a test set; and successively inputting the training set and the test set into a hybrid network of a convolutional neural network and a long short-term memory network for training and testing.

Single-ended fault positioning method and system for high-voltage direct-current transmission line of hybrid network

A transformer failure diagnosis method and system based on an integrated deep belief network are provided. The disclosure relates to the fields of electronic circuit engineering and computer vision. The method includes the following: obtaining a plurality of vibration signals of transformers of various types exhibiting different failure types, retrieving a feature of each of the vibration signals, and establishing training data through the retrieved features; training a plurality of deep belief networks exhibiting different learning rates through the training data and obtaining a failure diagnosis correct rate of each of the deep belief networks; and keeping target deep belief networks corresponding to the failure diagnosis correct rates that satisfy requirements, building an integrated deep belief network through each of the target deep belief networks, and performing a failure diagnosis on the transformers through the integrated deep belief network.

Transformer failure diagnosis method and system based on integrated deep belief network

A method and a system for diagnosing an open circuit (OC) fault of an insulated gate bipolar transistor (IGBT) of a T-type three-level (T3L) inverter under multiple power factors based on instantaneous current distortion are provided. Similar characteristics of current distortion may be caused by an OC fault of a T3L inverter, making it is difficult to locate the fault. The method for diagnosing an OC fault of a grid-connected T3L inverter, can diagnose the OC fault hierarchically; four switch transistors in a phase can be divided into two groups according to the similarity analysis of current distortion under different power factors; group-based fault diagnosis is realized by half cycles in which a zero domain occurs; and then, a specific switching signal is injected to realize equipment-based OC fault diagnosis. The OC fault diagnosis of a T3L inverter is realized without additional hardware circuits.

Method and system for diagnosing open circuit (OC) fault of T-type three-level (T3L) inverter under multiple power factors

A method and a system for comprehensively evaluating reliability of a multi-chip parallel IGBT module are provided. The method includes: establishing a gate-emitter voltage reliability model of the multi-chip parallel IGBT module, performing a chip fatigue failure test, and selecting a gate-emitter voltage as a failure characteristic quantity; establishing a transconductance reliability model of the multi-chip parallel IGBT module, performing a bonding wire shedding failure test, and selecting a transmission characteristic curve of the module as a failure characteristic quantity; using a Pearson correlation coefficient to characterize a degree of health of the IGBT module, and respectively calculating degrees of health PPMCCand PPMCCin different degrees of chip fatigue and bonding wire shedding failure states; and comprehensively evaluating the reliability of the multi-chip parallel IGBT module according to PPMCCand PPMCC.

Method and system for comprehensively evaluating reliability of multi-chip parallel IGBT module

A method and system for predicting an insulated gate bipolar transistor (IGBT) lifetime based on compound failure mode coupling are provided. First, a simultaneous failure probability model of a bonding wire and a solder layer is calculated. Next, expectancy of the simultaneous failure probability model is calculated and recorded as a lifetime under a coupling effect. A coupling function relation is established. A lifetime of the solder layer and a lifetime of the bonding wire are predicted. An IGBT lifetime prediction model not taking the coupling effect into account is established. An IGBT lifetime prediction model taking the coupling effect into account is established. In the disclosure, the lifetime of the IGBT module under the coupling effect of the solder layer and the bonding wire may be accurately predicted.

Method and system for predicting insulated gate bipolar transistor lifetime based on compound failure mode coupling

A transformer fault diagnosis method and system using induced ordered weighted evidence reasoning is provided. The method includes the following steps. A typical data sample of transformer sweep frequency response analysis is loaded and a diagnostic label is set as an identification framework. Test data of a device to be diagnosed is loaded. Basic probability assignment is calculated and a reliability decision matrix is constructed. An induced ordered weighted averaging operator and its induction vector are calculated according to a sample source of the data. An index weight vector is calculated. All evidence is fused by the induced ordered weighted evidence theory and reliability of comprehensive evaluation is calculated, so as to determine a diagnosis result. The disclosure realizes fault identification, fault type distinction and fault position of power equipment by interpreting detection waveforms.

Transformer fault diagnosis method and system using induced ordered weighted evidence reasoning

A method and a system for fault diagnosis with small samples of power equipment based on virtual and real twin spaces are disclosed, which belong to the field of fault diagnosis of power equipment. The method includes: test samples containing different locations, types and severity levels of fault of power equipment are acquired to form a real physical space; a virtual mirror space is acquired by simulation according to a simulation model of the equipment to be diagnosed; the training set in the real physical space is spatially integrated with the sample set in the virtual mirror space to obtain a training sample set in the twin spaces; the training sample set in the twin spaces serves as the supplement to the training set in the real physical space, and the fault type and fault location serve as diagnostic labels to be input to the deep neural network for training.

Method and system for fault diagnosis with small samples of power equipment based on virtual and real twin spaces

A method and a system for predicting a gas content in transformer oil based on a joint model are provided and belong a field of transformer failure prediction. The method includes the following: determining a type and a time series of gas to be predicted related to a failure, processing an original series by adopting empirical mode decomposition (EMD) and local mean decomposition (LMD) for a non-stationarity characteristic of a dissolved gas concentration series in oil; performing normalization on each sub-series component, dividing a training sample and a test sample; and establishing a deep belief network (DBN) prediction model for each of the sub-series components for training, performing superposition and reconstruction on the established DBN prediction model to perform characteristic extraction and classification on multi-dimensional data of the failure, evaluating prediction performance of the prediction model through calculating an error index.

Method and system for predicting gas content in transformer oil based on joint model

A reliability evaluation method and system for a microgrid inverter IGBT based on segmented long short-term memory (LSTM) is disclosed, including steps as follows. An electrothermal coupling model is constructed to obtain real-time junction temperature data. The original LSTM algorithm is improved to obtain a segmented LSTM prediction network for the aging characteristics of the IGBT. The monitoring value of the IGBT aging parameter is used to perform segmented LSTM prediction to obtain the predicted aging process, and the threshold values of different aging stages are categorized. An aging correction is performed on the aging parameter of the electrothermal coupling model to ensure the accuracy of the junction temperature data. Rainflow-counting algorithm is used to calculate real-time thermal stress load distribution of the IGBT. The fatigue damage theory and the Lesit life prediction model are combined to calculate the real-time cumulative damage and predicted life of the IGBT.

Reliability evaluation method and system of microgrid inverter IGBT based on segmented LSTM

A method and a system of predicting an electric system load based on wavelet noise reduction and empirical mode decomposition-autoregressive integrated moving average (EMD-ARIMA) are provided. The method and the system belong to a field of electric system load prediction. The method includes the following steps. Raw load data of an electric system is obtained first. Next, noise reduction processing is performed on the load data through wavelet analysis. The noise-reduced load data is further processed through an EMD method to obtain different load components. Finally, ARIMA models corresponding to the different load components are built. Further, the ARIMA models are optimized through an Akaike information criterion (AIC) and a Bayesian information criterion (BIC). The load components obtained through predicting the different ARIMA models are reconstructed to obtain a final prediction result, and accuracy of load prediction is therefore effectively improved.

Method and system of predicting electric system load based on wavelet noise reduction and EMD-ARIMA

The invention discloses a failure diagnosis method for a power transformer winding based on a GSMallat-NIN-CNN network. The failure diagnosis method includes: measuring a vibration condition of the transformer winding by using a multi-channel sensor to obtain multi-source vibration data of the transformer; converting the multi-source vibration data obtained through measurement into gray-scale images through GST gray-scale conversion; decomposing, by using a Mallat algorithm, each gray-scale image layer by layer into a high-frequency component sub-image and a low-frequency component sub-image, and fusing the sub-images; reconstructing fused gray-scale images, and coding vibration gray-scale images according to respective failure states of the transformer winding; establishing a failure diagnosis model for the transformer based on the GSMallat-NIN-CNN network; and randomly initializing network parameters to divide a training set and a test set, and training and tuning the network by using the training set; and testing the trained network by using the test set.

Failure diagnosis method for power transformer winding based on GSMallat-NIN-CNN network

A method and a system for diagnosing a fault of a three-phase three-level rectifier are relate to the technical field of fault diagnosis of power electronic equipment, and provided to implement identification and location of an open-circuit fault of a power switching device thereof. A deviation between an expected value and an actual value of a phase-to-phase voltage is adopted as a diagnosis variable. The diagnosis variable is calculated by adopting a screening technique, thereby reducing calculation error to ensure accuracy of diagnosis. Only existing voltage current signals in a control system of the rectifier are required to calculate the diagnosis variable, so no additional hardware is required and low-cost fault diagnosis can be implemented. Different voltage thresholds are adopted for different fault characteristic sections, and the voltage thresholds are updated in real time according to a direct current side voltage, which improves diagnosis speed while ensuring higher robustness.

Method and system for diagnosing open-circuit fault of power switching device of three-phase three-level rectifier

A wind power prediction method and system for optimizing a deep Transformer network by whale optimization algorithm are disclosed. The sequence data of wind power and related influence factors are taken as sample data which is divided into a training set and a test set, where the data is trained and predicted by a Transformer network model established according to values of the initialized hyper-parameters, and an average absolute error of wind power prediction is taken as a fitness value of each whale group. A local optimal position is determined according to the initial fitness value of individual whale group, and the current optimal position is updated by utilizing whale group optimization, and the best prediction effect is obtained by comparing the local optimal solution with the global optimal solution. An optimal hyper-parameter combination is obtained after multiple iterations of the whale optimization algorithm, and the wind power is predicted.

Wind power prediction method and system for optimizing deep transformer network

A DC/DC converter fault diagnosis method based on an improved sparrow search algorithm, includes: establishing an simulation module of the converter, selecting a leakage inductance current of a transformer as a diagnosis signal, and collecting diagnosis signal samples under OC faults of different power switching devices of the converter as a sample set; improving a global search ability of a sparrow search algorithm by using a Levy flight strategy; dividing the sample set into a training set and a test set, preliminarily establishing an architecture of a deep belief network, and initializing network parameters; optimizing a quantity of hidden-layer units of the deep belief network by using an improved sparrow search algorithm, to obtain a best quantity of hidden-layer units of the deep belief network; and training an optimized deep belief network obtained based on the improved sparrow search algorithm, and obtaining a fault diagnosis result based on a trained network.

Direct current (DC)/DC converter fault diagnosis method and system based on improved sparrow search algorithm

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Hubei, China

Yigang He