Fault diagnosis of transmission lines via adaptive modal filtering and an enhanced convolutional neural network synergistic approach

In order to make the transmission line fault diagnosis more intelligent and adapt to the needs of big data, this paper proposes a transmission line fault diagnosis method based on variational mode decomposition (VMD) and bidirectional long-short-term memory network (BiLSTM). The bidirectional longterm and short-term memory network includes a bidirectional LSTM layer, a fully connected layer, and a softmax layer. The zero-sequence current after line fault is extracted, and it is subjected to variational modal decomposition. The decomposed modal signal and the original signal are input into the bidirectional long-short-term memory network for fault diagnosis. Through simulation comparison with other fault diagnosis methods, it is proved that the method of this paper has higher diagnostic accuracy and the method of this paper has high stability.

Fault diagnosis constitutes a problem in electric power systems with relevant economic impact for operators and stakeholders. Artificial neural networks have been proposed in the literature to deal with this problem in a significant number of applications. However, most proposals are based in ad-hoc structure specification and model regularization, which compromises the direct application of the algorithms to other transmission systems. Given the above, this paper will present the application of Autonomous Bayesian Neural models to fault diagnosis in electric power system transmission lines. This modeling strategy includes automatic and analytical procedures for input selection, model specification and regularization without the need of a validation set. The system performance will be analyzed considering data from a real transmission line of the Brazilian interconnected system.

In response to the problems of limited algorithms and low diagnostic accuracy for fault diagnosis in large tractor transmission systems, as well as the high noise levels in tractor working environments, a defect detection approach for tractor transmission systems is proposed using an enhanced convolutional neural network (CNN) and a bidirectional long short-term memory neural network (BILSTM). This approach uses a one-dimensional convolutional neural network (1DCNN) to create three feature extractors of varying scales, directly extracting feature information from different levels of the raw vibration signals. Simultaneously, in order to enhance the model’s predicted accuracy and learn the data features more effectively, it presents the multi-head attention mechanism (MHA). To overcome the issue of high noise levels in tractor working environments and enhance the model’s robustness, an adaptive soft threshold is introduced. Finally, to recognize and classify faults, the fused feature data are fed into a classifier made up of bidirectional long short-term memory (BILSTM) and fully linked layers. The analytical findings demonstrate that the fault recognition accuracy of the method described in this article is over 98%, and it also has better performance in noisy environments.

In this paper, the primary focus is of Slot Tagging of Gujarat Dialogue, which enables the Gujarati language communication between human and machine, allowing machines to perform given task and provide desired output. The accuracy of tagging entirely depends on bifurcation of slots and word embedding. It is also very challenging for a researcher to do proper slot tagging as dialogue and speech differs from human to human, which makes the slot tagging methodology more complex. Various deep learning models are available for slot tagging for the researchers, however, in the instant paper it mainly focuses on Long Short-Term Memory (LSTM), Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) and Long Short-Term Memory – Conditional Random Field (LSTM-CRF), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network - Bidirectional Long Short-Term Memory (CNN-BiLSTM) and Bidirectional Long Short-Term Memory – Conditional Random Field (BiLSTM-CRF). While comparing the above models with each other, it is observed that BiLSTM models performs better than LSTM models by a variation ~2% of its F1-measure, as it contains an additional layer which formulates the word string to traverse from backward to forward. Within BiLSTM models, BiLSTM-CRF has outperformed other two Bi-LSTM models. Its F1-measure is better than CNN-BiLSTM by 1.2% and BiLSTM by 2.4%.KeywordsSpoken Language Understanding (SLU)Long Short-Term Memory (LSTM)Slot taggingBidirectional Long Short-Term Memory (BiLSTM)Convolutional Neural Network - Bidirectional Long Short-Term Memory (CNN-BiLSTM)Bidirectional Long Short-Term Memory (BiLSTM-CRF)

Generalized fault diagnosis method of transmission lines using transfer learning technique

A bearing is a critical component in the transmission of rotating machinery. However, due to prolonged exposure to heavy loads and high-speed environments, rolling bearings are highly susceptible to faults, Hence, it is crucial to enhance bearing fault diagnosis to ensure safe and reliable operation of rotating machinery. In order to achieve this, a rotating machinery fault diagnosis method based on a deep convolutional neural network (DCNN) and Whale Optimization Algorithm (WOA) optimized Deep Extreme Learning Machine (DELM) is proposed in this paper. DCNN is a combination of the Efficient Channel Attention Net (ECA-Net) and Bi-directional Long Short-Term Memory (BiLSTM). In this method, firstly, a DCNN classification network is constructed. The ECA-Net and BiLSTM are brought into the deep convolutional neural network to extract critical features. Next, the WOA is used to optimize the weight of the initial input layer of DELM to build the WOA-DELM classifier model. Finally, the features extracted by the Improved DCNN (IDCNN) are sent to the WOA-DELM model for bearing fault diagnosis. The diagnostic capability of the proposed IDCNN-WOA-DELM method was evaluated through multiple-condition fault diagnosis experiments using the CWRU-bearing dataset with various settings, and comparative tests against other methods were conducted as well. The results indicate that the proposed method demonstrates good diagnostic performance.

In order to adaptively fuse various electrical quantities generated during the operation of transmission lines, the complementary information of each electrical quantity is combined to improve the accuracy of fault diagnosis of transmission lines. This paper proposes a method of line fault diagnosis based on deep convolutional neural network and multi-kernel learning. First, use the convolutional neural network to automatically extract the features of various electrical quantities, and then map the extracted features to different feature spaces, and finally use the multi-kernel learning method to build a generalization model fusing multi-source feature data. The weight of different features we learned is used to clarify the different contributions of each characteristic data source to the fault diagnosis. The results showed that this method has higher classification accuracy for multi-source electrical quantities, and achieves an improvement in overall diagnostic performance.KeywordsTransmission lineFault diagnosisConvolutional neural networkMulti-kernel learning

With the accelerated construction of new power systems, the scale and complexity of transmission systems are increasing, while transmission line fault diagnosis algorithms for complex power timing data urgently need to be studied and tested.In this paper, a transmission line fault diagnosis model fusing convolutional neural network (CNN) and long short-term memory network (LSTM) is proposed. The model realizes automatic extraction of local spatio-temporal features of fault signals through convolutional neural network, captures the long-term dependence of power system operation state by using long and short-term memory network, and introduces an attention mechanism to enhance the characterization of key fault features, which effectively improves the identification accuracy of the fault types of the transmission line under the complex working conditions. It adapts to the requirements of smart grid real-time monitoring scenarios on algorithm robustness. Also, it considers the use of the synthetic few over-sampling technique (SMOTE) to realize the data enhancement to improve the imbalance of data distribution. The results show that compared with the traditional connection neural network, back propagation neural network, and LSTM model, the CNN-LSTM model is only 0.0497 in the loss value, which is reduced by about 70%, and the fault identification accuracy and precision rate are more than 97%, which is improved by about 2%. It provides theoretical support for solving the problem of real-time fault diagnosis of large-scale transmission systems, and has significant engineering practice value for reducing the cost of grid operation and maintenance and improving the reliability of power supply.

As the scale of the transmission system continues to expand, the structure and fault characteristics of the grid become more and more complex, and the existing fault diagnosis methods have difficulty in extracting the fault characteristics accurately in the face of the complex grid. A convolutional neural network (CNN) based fault diagnosis method is proposed. Firstly, the network structure is tested step by step through layer-by-layer filtering and layer-by-layer incremental stacking, with the aim of building a network structure fully adapted to grid fault diagnosis; then the training parameters are adjusted by optimization strategies; finally, an AC transmission system model is built on the MATLAB/SIMULINK platform, and the diagnostic effect is demonstrated by combining the network with the fault diagnosis. It is proved that this method has a high diagnostic accuracy and also reduces the number of fault samples, which is more in line with the practical needs.

A survey on intelligent system application to fault diagnosis in electric power system transmission lines

This study introduced a novel real-time Fault Diagnosis Model (FDM) in manufacturing robots by integrating Depthwise Convolutional Neural Networks (CNNs) with Bidirectional Long Short-Term Memory (BiLSTM) networks. The objective is to design a model that can handle the complex high-dimensional sensor data that arrives out of complex, non-linear systems for effective FDM. The work introduced a Feature Extraction (FE) model based on Monte Carlo Filtering (MCF). The work integrates a Depthwise CNN with BiLSTM (DC-BiLSTM) for diagnosis. The integration helps to reduce the computational need and, at the same time, preserve the feature representation. The model was experimented against other models, such as CNN, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Feed-Forward Neural Networks (FFNN), using a fault dataset sourced from a simulated environment. The results have shown that the proposed model fared well in terms of accuracy, precision, recall, and F1 score against all compared models. The results have judged the proposed model’s applicability in the field of fault diagnosis, which could effectively predict mishaps in advance, thereby helping with efficient maintenance and ensuring continuous productivity.

Fault diagnosis in transmission lines constitutes a problem in power systems with relevant technical and economic impact for independent system operators and also for the asset owner itself. Several techniques have been proposed in the literature to deal with this problem in a significant number of applications. This survey presents the main research areas and the trends in transmission line fault diagnosis in power systems. Classification of strategies and their relationship with applications are shown and discussed.

The environment in which transmission lines are located is complex and harsh, and various types of faults are prone to occur. In order to prevent the further expansion of the scope of accidents caused by transmission line faults, this paper proposes a transmission line fault diagnosis method combining Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU). Firstly, a fault diagnosis model of transmission line based on CNN-GRU is established to train and test the collected data; then, a simulation test system of transmission line is established, the parameters of the system and line are designed, and the transmission line is proposed. Finally, accurate fault diagnosis is carried out by simulating the test system and the diagnostic model. The experimental results show that the accuracy of fault diagnosis using this method is very high, and it can guide the daily operation and maintenance of actual transmission lines.

Bearing fault diagnosis and prognosis are crucial for the effective management of industrial equipment. Due to the automatic feature extraction of Deep Learning (DL) models, many recent studies have focused on using DL for these tasks. However, most studies address only one of these tasks. This study aims to present DL models and their powerful ML tools capable of both fault diagnosis and prognosis on industrial equipment. To identify the best DL model for both tasks, a comparative study is conducted on various DL models and ML tools, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN in parallel with LSTM (CNN-LSTM), Bidirectional LSTM (Bi-LSTM), and transformer models. The ML tools investigated include Recurrent Dropout, Residual Network (ResNet), and Monte Carlo Dropout (MC Dropout). These models are validated using online datasets from Case Western Reserve University (CWRU) and Xi’an Jiao Tong University (XJTU-SY) for the task of fault diagnosis. For fault prognosis, datasets from XJTU-SY and IEEE PHM are used. The results demonstrate the superiority of the ResCNN-LSTM model in both fault diagnosis and prognosis tasks. It achieves prediction accuracy of 99.87% and 96.39% and F1-scores of 0.998 and 0.964 for fault diagnosis on the CWRU and XJTU-SY datasets, respectively. Additionally, it shows a Root Mean Square Error (RMSE) of 8.56 and Mean Absolute Error (MAE) of 12.16 for fault prognosis on the XJTU dataset, and an RMSE of 12.18 using the IEEE PHM bearing dataset. These high performance metrics indicate the model's effectiveness in accurately diagnosing faults and predicting failures.

In this work, an improved attention mechanism rolling element bearing (REB) fault diagnosis method based on a convolutional neural network (CNN) and a Bi-directional long-short term memory (BiLSTM) is proposed. The original REB fault signals of 10 different fault types are selected from the bearing standard database of Case Western Reserve University (CWRU). The variational mode decomposition optimized by crested porcupine optimizer algorithm (CPO-VMD) is used for pre-processing REB fault signals into multiple intrinsic mode functions (IMF). The Squeeze-and-Excitation (SE) attention is inserted into the convolutional module to adjust the weight relationship between channels, thereby extracting more feature information. After the BiLSTM network structure, self-attention is introduced to focus on important fault characteristics. The experimental results show that the training time of the CNN-SENet-BiLSTM-Self-Attention REB fault diagnosis model proposed in this paper is 39 s, with an accuracy of 99.67%. Compared with the traditional CNN-LSTM, CNN-BiLSTM, CNN-SENet-BiLSTM, CNN-BiLSTM-Self-Attention, and CNN-SENet-LSTM-Self-Attention models, the accuracy rates increase by 9%, 8%, 1.7%, 0.7%, and 0.3%, respectively.