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.
The difference between the actual feeder parameters and feeder parameter data stored in a database or offered by manufacturers is significant owing to the ambient environment, temperature, and skin ...effect. Here, a parameter estimation method is proposed for unbalanced three‐phase distribution feeders based on the bus voltages and branch power flows measured from two terminals of the feeder. In the proposed method, a high‐precision phasor measurement unit is not required to estimate the magnitude and phase angle of the phasor quantity using a common time source for synchronisation. A radial basis function neural network with multi‐run optimisation (RBFNN‐MRO) is proposed to map the complex nonlinear relations between the distribution feeder parameters and electrical quantities. The feasibility and performance of the proposed RBFNN‐MRO method were verified using the four IEEE test systems. The comparison between the proposed RBFNN‐MRO method and the multi‐run method based on the quasi‐Newton method is implemented via the maximum absolute percentage error (MAPE) curves. The results reveal that the proposed RBFNN‐MRO method has excellent potential for improving the accuracy of feeder parameter estimation, even for bad data preparation.
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.
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.
As an essential apparatus in resonance grounding systems, the arc suppression coil is widely used in rejecting the single‐line‐to‐ground (SLG) fault of distribution networks. However, the resistive ...ground‐fault current cannot be compensated by an arc suppression coil (ASC). As a novel active arc suppression device, the hybrid flexible arc suppression device (HFASD) composed of ASC and flexible arc suppression device (FASD) can eliminate the capacitive component of ground‐fault current and the resistive component of ground‐fault current. After the SLG fault occurs, the FASD from stand‐alone mode to grid‐connected mode may lead to switching transients, affecting the stability of distribution networks. The soft grid connection (SGC) strategy can achieve low or without transient response during the grid synchronization of FASD to the grid. Thus, the HFASD based on SGC strategy and segmented proportional‐integral‐differential (SPID) and second‐order generalized integrator phase‐locked loop (SOGI‐PLL) control method is proposed for rejecting the current and voltage overshoot responses during the process of grid synchronization of FASD in this paper. Simulation and experimental results verify the effectiveness of the proposed method, and the benefit of the proposed method is indicated in comparison with the non‐SGC (NSGC) strategy.
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.
The diagnosis of high-impedance fault (HIF) is a critical challenge due to the presence of faint signals that exhibit distortion and randomness. In this study, we propose a novel diagnostic approach ...for HIF based on semantic segmentation of the signal envelope (SE) and Hilbert marginal spectrum (HMS). The proposed approach uses 1D-UNet to identify the transient process of potential fault events in zero-sequence voltage to judge fault inception. Longer timescale zero-sequence voltage is then used to extract SE and HMS, representing HIF distortion and randomness characteristics. These features are transformed into images, and ResNet18 is employed to detect the presence of HIF. An industrial prototype of the proposed approach has been implemented and validated in a 10 kV test system. The experimental results indicate that the proposed approach outperforms the comparison by a significant margin regarding triggering deviation and detection accuracy, particularly in resonant distribution networks.
Many sensors like digital fault indicators (DFIs) have been applied and promoted in distribution systems. The sensors can provide a technical mean for single‐line‐to‐ground (SLG) fault section ...location, but there are still some feature extraction and fault diagnosis problems. A novel SLG fault section location method utilizing auto‐encoder (AE) and fuzzy C‐means (FCM) clustering is presented in this work. Taking advantage of abundant information provided by DFIs, striking features can be extracted by the AE network, which is different from the artificially designed features that rely on prior knowledge. Compared with the learning‐based methods requiring massive training data, the proposed method only requires the data from one SLG fault. By applying the AE network to the zero‐sequence current measured by DFIs, the SLG fault section location's striking features could be obtained. Through feature classification by FCM clustering without setting threshold, the positional relationship between each detection node and the fault point would be distinguished to locate the fault section. Considering the abnormal communication of DFIs, the experiment proves that the proposed method can work effectively under various fault conditions.
•Single-phase flexible arc suppression device (SFASD) based on cascaded H-bridge topology.•Backstepping control and second-order generalized integrator phase-locked loop (BSC-SOGI-PLL) method is ...proposed.•BSC-SOGI-PLL and BSC method applied to SFASD are compared, respectively.•Decentralized control is applied to SFASD.•SFASD based on BSC-SOGI-PLL method rejects the ground-fault current in experiment testing.
The single line-to-ground (SLG) fault is a research focus, considering it is amongst the top frequent occurrences of faults in distribution systems. The neutral point treatment is crucial for continuous safe operation of distribution systems. However, the traditional arc suppression coil cannot compensate for the active component in ground current. Therefore, we present a single-phase flexible arc suppression device (SFASD) based on backstepping control and second-order generalized integrator phase-locked loop (BSC-SOGI-PLL) method. The SFASD with a cascaded H-bridge (CHB) topology is connected to the neutral point of distribution systems for eliminating the ground current. The comparisons between the BSC-SOGI-PLL and the BSC method concerning the effect of arc suppression are discussed. The effectiveness of the proposed SFASD based on the BSC-SOGI-PLL components for arc suppression is verified on the scale-down experimental platform.
Nowadays, smart monitoring devices such as digital fault indicator (DFI) have been installed in distribution systems to provide sufficient information for fault location. However, it is still a ...challenge to extract effective features from massive data for single-line-to-ground (SLG) fault-section location. This work proposes a novel method of fault-section location using a 1-D convolutional neural network (1-D CNN) and waveform concatenation. After SLG fault occurs, DFI measures the transient zero-sequence currents at double-ends of the line section, which could be concatenated to construct characteristic waveform. The features of characteristic waveforms would be extracted adaptively by 1-D CNN to locate the fault section. Furthermore, the problem where the on-site recorded data are hard to collect would be solved because 1-D CNN only needs a small number of samples for training in practical applications. The experimental results verified that the proposed method could work effectively under various fault conditions, even if a few DFIs are out of order.
PDF
