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Mou-Fa Guo

Bio: Mou-Fa Guo is an academic researcher from Fuzhou University. The author has contributed to research in topics: Fault (power engineering) & Computer science. The author has an hindex of 6, co-authored 12 publications receiving 221 citations.

Papers
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Journal ArticleDOI
TL;DR: In this paper, a simple and effective method of faulty feeder detection in resonant grounding distribution systems based on the continuous wavelet transform (CWT) and convolutional neural network (CNN) is presented.
Abstract: Feature extraction for fault signals is critical and difficult in all kinds of fault detection schemes. A novel simple and effective method of faulty feeder detection in resonant grounding distribution systems based on the continuous wavelet transform (CWT) and convolutional neural network (CNN) is presented in this paper. The time-frequency gray scale images are acquired by applying the CWT to the collected transient zero-sequence current signals of the faulty feeder and sound feeders. The features of the gray scale image will be extracted adaptively by the CNN, which is trained by a large number of gray scale images under various kinds of fault conditions and factors. The features extraction and the faulty feeder detection can be implemented by the trained CNN simultaneously. As a comparison, two faulty feeder detection methods based on artificial feature extraction and traditional machine learning are introduced. A practical resonant grounding distribution system is simulated in power systems computer aided design/electromagnetic transients including DC, the effectiveness and performance of the proposed faulty feeder detection method is compared and verified under different fault circumstances.

206 citations

Journal ArticleDOI
TL;DR: A deep-learning-based fault classification method in small current grounding power distribution systems is presented and has the characteristics of high accuracy and adaptability in fault classification of power Distribution systems.
Abstract: Fault classification is important for the fault cause analysis and faster power supply restoration. A deep-learning-based fault classification method in small current grounding power distribution systems is presented in this paper. The current and voltage signals are sampled at a substation when a fault occurred. The time-frequency energy matrix is constructed via applying Hilbert–Huang transform (HHT) band-pass filter to those sampled fault signals. Regarding the time-frequency energy matrix as the pixel matrix of digital image, a method for image similarity recognition based on convolution neural network (CNN) is used for fault classification. The presented method can extract the features of fault signals and accurately classify ten types of short-circuit faults, simultaneously. Two simulation models are established in the PSCAD/EMTDC and physical system environment, respectively. The performance of the presented method is studied in the MATLAB environment. Various kinds of fault conditions and factors including asynchronous sampling, different network structures, distribution generators access, and so on are considered to verify the adaptability of the presented method. The results of investigation show that the presented method has the characteristics of high accuracy and adaptability in fault classification of power distribution systems.

104 citations

Journal ArticleDOI
TL;DR: By analyzing the zero-sequence voltage under normal conditions and fault conditions, a discrete wavelet transform-based triggering algorithm is proposed and it has high reliability and it can meet the needs of real-time monitoring.
Abstract: Earth faults occur frequently in power distribution systems and they are usually accompanied by an arc. This is a big hazard to the power distribution systems. Effective early detection is difficult to achieve using conventional methods, which brings inconvenience to fault location and maintenance. In order to solve this problem, a sensitive triggering algorithm should be equipped in the detection device. By analyzing the zero-sequence voltage under normal conditions and fault conditions, a discrete wavelet transform-based triggering algorithm is proposed in this paper. A great number of waveforms simulated by the PSCAD/EMTDC software are used to test the algorithm and two other traditional algorithms, and the results are analyzed and compared. In addition, the curves recorded in the physical simulation systems and the field power distribution systems are input into the proposed algorithm to test its adaptability. For testing whether the algorithm could achieve real-time triggering, a device is designed to carry the algorithm program. These experiments show that the proposed algorithm has high reliability and it can meet the needs of real-time monitoring.

49 citations

Journal ArticleDOI
TL;DR: An arc-suppression method based on the improved finite control set model predictive control achieves an excellent performance of suppressing fault current and extinguishing arc and the balance of switching transitions is also achieved.
Abstract: Traditional arc-suppression devices have a weak effect on the arc-extinguishing result of earth fault because of increased active and harmonic components of fault current. To solve the problem, in this paper, a flexible arc-suppression device based on a three-phase cascaded H-bridge (CHB) converter with auxiliary sources is further developed; on this basis, an arc-suppression method based on the improved finite control set model predictive control is proposed. The proposed approach, which uses a combination of two voltage levels in a sampling period to reduce the steady-state current error, controls the CHB converter to inject compensation current into the distribution network. Taking into account the reduction of switching losses and balancing heat in each H-bridge cell, a novel method that causes the switching transitions to be distributed evenly among the H-bridge cells is proposed to select the optimal switching combination. The tracking capability of the improved control method is analyzed, and the parameters affecting the arc-suppression performance are obtained. The simulation and experimental results show that the proposed method achieves an excellent performance of suppressing fault current and extinguishing arc. Moreover, the balance of switching transitions is also achieved.

42 citations

Journal ArticleDOI
TL;DR: An effective and stable HGU fault diagnosis system using one-dimensional convolutional neural network (1-D CNN) and gated recurrent unit (GRU) based on the sequence data structure is proposed and the fault diagnostic model, which is trained by the practical vibration signal, has successfully applied in engineering practice.
Abstract: Machine learning algorithm based on hand-crafted features from the raw vibration signal has shown promising results in the hydroelectric generating unit (HGU) fault diagnosis in recent years. Such methodologies, nevertheless, can lead to important information loss in representing the vibration signal, which intrinsically relies on engineering experience of diagnostic experts and prior knowledge about feature extraction techniques. Therefore, in this paper, an effective and stable HGU fault diagnosis system using one-dimensional convolutional neural network (1-D CNN) and gated recurrent unit (GRU) based on the sequence data structure is proposed. First, the raw vibration data is reconstructed by data segmentation, which can improve training efficiency. Second, the reconstruction data under the influence of different running conditions and various fault factors can be effectively and adaptively learned by 1-D CNN-GRU and then determine information fault categories via network inference. Finally, four machine learning methods are applied to diagnosis the reconstruction data based on the experimental dataset. The performance of the proposed method is verified by comparing with the results of other machine learning techniques. Furthermore, the fault diagnostic model, which is trained by the practical vibration signal, has successfully applied in engineering practice.

35 citations


Cited by
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Journal ArticleDOI
TL;DR: The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data and suggests the possibility of transfer learning across PHM applications.
Abstract: Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.

121 citations

Journal ArticleDOI
TL;DR: A general overview of AI, including its definitions, history and state-of-the-art methodologies, and a comprehensive review of its applications to security assessment, stability assessment, fault diagnosis, and stability control in smart grids are presented.

102 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a feature distance stack autoencoder (FD-SAE) for rolling bearing fault diagnosis, which has stronger feature extraction ability and faster network convergence speed.
Abstract: In recent years, autoencoder has been widely used for the fault diagnosis of mechanical equipment because of its excellent performance in feature extraction and dimension reduction; however, the original autoencoder only has limited feature extraction ability due to the lack of label information. To solve this issue, this study proposes a feature distance stack autoencoder (FD-SAE) for rolling bearing fault diagnosis. Compared with the existing methods, FD-SAE has stronger feature extraction ability and faster network convergence speed. By analyzing the characteristics of original rolling bearing data, it is found that there are evident differences between normal data and faulty data. Therefore, a simple linear support vector machine (SVM) is used to classify normal data and faulty data, and then the proposed FD-SAE is used for fault classification. The novel combination of SVM and FD-SAE has simple structure and little computational complexity. Finally, the proposed method is verified on the rolling bearing data set of Case Western Reserve University (CWRU).

99 citations

Journal ArticleDOI
TL;DR: The results show that the proposed fault location method by considering double-end unsynchronized using Hilbert–Huang transform and one-dimensional convolutional neural network (1D-CNN) can reliably and accurately locate line faults under fault resistance up to 5200 Ω.
Abstract: Due to the difficulty in locating high-resistance grounding faults, this paper proposes a novel fault location method for HVdc transmission lines by considering double-end unsynchronized using Hilbert–Huang transform and one-dimensional convolutional neural network (1D-CNN). After the fault signal is collected at both ends, the proposed method can achieve high-precision fault location, requiring only the two ends data transmission without time synchronization. After Empirical Mode Decomposition (EMD), the high-frequency components of the double-terminal fault signals are connected in series to make a characteristic waveform. This waveform contains characteristics of different fault types and distances, which can be learned by CNN. The trained CNN can then be used to achieve fault location effectively. As a comparison, two fault location methods based on traditional traveling wave and machine learning are introduced. Electromagnetic transient simulation software PSCAD/EMTDC has been used to carry out various types of fault simulation on the ± 500 kV HVdc transmission system. The results show that the proposed method can reliably and accurately locate line faults under fault resistance up to 5200 Ω.

73 citations

Journal ArticleDOI
18 Jan 2021-Entropy
TL;DR: Wang et al. as mentioned in this paper proposed an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN), which achieved an overall performance of 70.75, 67.47, 68.76, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively.
Abstract: Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

73 citations