The application of conventional sensors is limited due to energy supply issues and environmental problems, and current triboelectric nanogenerator (TENG) based self-powered sensing systems is hard to meet the specific requirement. In this paper, TiO2/Ni/C composites were prepared using MOF-derived Ni/C materials for SO2F2 detection at room temperature. The excellent pore structure and high electrical conductivity of Ni/C material greatly improve the sensitivity properties of TiO2. The TiO2/Ni/C composites-based SO2F2 sensor exhibits a wide detection range of 5–200 ppm concentration and a fast response/recovery time of 26 s/9 s, while its response value for 200 ppm SO2F2 is 18.34 times higher than that of the pure TiO2-based sensor. Furthermore, by modifying the PVDF film with TiO2, the permittivity of PVDF is greatly improved, and the surface potential is 2.6 times higher than that of pure PVDF film. Eventually, a dual-layer hybrid TENG based on TiO2/PVDF composite film was constructed. Its upper TENG powers the TiO2/Ni/C composite sensor, and the lower hybrid TENG drives the thermohygrometer to realize a self-powered temperature and humidity-corrected SO2F2 sensing system, which can accurately implement SO2F2 gas concentration alarms at 10 ppm, 50 ppm, and 100 ppm at different humidity levels. This work broadens the application of TENG in the field of self-powered gas sensing while providing a new idea to achieve the trace detection of SO2F2 at room temperature. 

Hybrid nanogenerator driven self-powered SO2F2 sensing system based on TiO2/Ni/C composites at room temperature

The feeders in distribution networks can be generally classified into two types: homogenous and hybrid feeders. Hybrid feeders consist of non-homogeneous line segments. In this paper, a fault location scheme is proposed for distribution grids with both homogenous and hybrid feeders using the edge computing. The standard transient amplitude ratio arrays of the hybrid feeders are first constructed and stored at the edge nodes. Then, an accurate fault location model is trained by the radial basis function neural network and stored in the substation server. To achieve this goal, the transients are first sampled at the measurement nodes and transmitted to the edge nodes nearby. The amplitudes are then extracted to determine the faulty branch. For a hybrid feeder, the actual amplitude ratio array is compared with the standard one to identify the faulty line segment. Finally, the amplitude ratio of the two transient components with different frequencies at one terminal of the faulty section is computed and transmitted to the server. Based on the model of a distribution network built by PSCAD/EMTDC, extensive fault simulations are studied. The results demonstrate the effectiveness of the scheme.

Edge Computing-Based Fault Location in Distribution Networks by Using Asynchronous Transient Amplitudes at Limited Nodes

This paper proposes a new method to select single-line-to-ground (SLG) fault feeder in resonant grounding power distribution system based on compensation characteristic of extinction coil in different frequencies. The proposed method is insusceptible to the influence of device polarity reverse installation. Firstly, through analyzing the zero sequence equivalent circuit under a SLG fault, the difference of zero sequence current share in fundamental frequency and second-harmonic frequency is investigated. And then, according to the change of current share (CCS), fault feeder selection criterion and steps are established. Finally, numerous simulation tests demonstrate the effectiveness of the proposed method in identifying the faulty feeder or the faulty bus. Besides, its reliability is also verified by tests with different system parameters and different fault conditions. Specially, device polarity reverse installation tests and comparisons are also implemented, which shows the advantages of the proposed method in dealing with device polarity reverse installation.

Single-Line-to-Ground Fault Feeder Selection Considering Device Polarity Reverse Installation in Resonant Grounding System

The traditional arc suppression coil cannot compensate for the active and harmonic component of the ground fault current nor fully suppress the intermittent arc. For improvement, a novel full compensation method for the ground fault current of resonant grounded systems is proposed. The impedance of the compensation component is calculated based on the phase-to-ground insulation parameters, which are precisely measured by injecting a characteristic current signal at the neutral point. When a ground fault occurs, the faulty phase is identified by the phase angle of the ground fault current. Then, the compensation component is put in between the lagging phase of the faulty phase and ground. The faulty phase voltage is suppressed to zero by the input of the compensation component, and the ground fault current is fully compensated. Simulative results indicate that the phase-to-ground insulation parameters of distribution networks are accurately measured by the proposed method. The proposed full compensation method suppresses the faulty phase voltage rapidly, and all the components contained in the ground fault current are completely compensated reliably. The intermittent-arc ground fault is suppressed effectively without being affected by the fault resistance.

A novel full compensation method for the ground fault current of resonant grounded systems

Faulty feeder detection of single phase-to-ground fault for distribution networks based on improved K-means power angle clustering analysis

The single-phase-to-earth fault (SPTEF) current is weak in neutral ineffectively grounded systems. High fault impedance, noise and arc impair the fault feature, which increases the difficulty of faulty feeder detection. Based on the propagation characteristics of zero-mode transients (ZTs), this paper proposes a faulty feeder detection criterion by utilizing the amplitude information of the ZTs captured at both terminals. To conduct the criterion, the historical data of the ZTs recorded at both terminals of each feeder with a bus fault is collected. The standard amplitude ratios of the frequency components in the ZTs of each feeder are extracted by S-transform. After a SPTEF is detected in the system, the actual amplitude ratios of the ZTs are calculated and compared with the standard ones. Theoretical analysis and simulation indicate that the maximum ratio between the standard and actual ratios corresponds to the faulty feeder. In implementation of the criterion, both high sampling frequency and accurate synchronization are not required. A 10kV single-end radial distribution network is constructed by PSCAD/EMTDC. Various fault simulations are carried out and the calculation results demonstrate that the proposed criterion has high reliability and is not affected by neutral grounding mode, fault initial condition, high impedance fault (HIF) and noise.

/pdf/single-phase-to-earth-faulty-feeder-detection-in-power-3hnn7ge9g7.pdf

Single-Phase-to-Earth Faulty Feeder Detection in Power Distribution Network Based on Amplitude Ratio of Zero-Mode Transients

SO2F2 and SOF2 are important characteristic decomposition components of SF6. Accurate detection of these two gases is helpful for fault diagnosis of gas-insulated switchgear (GIS). In this article, the infrared characteristic peaks of SO2F2 and SOF2 were obtained by comparing the simulation and experimental infrared spectrum. Then, cross interference analysis was performed with SF6 characteristic components such as H2S, SO2, CO, and SF6 background gas to determine the appropriate detection bands for SO2F2 and SOF2. Tunable diode laser absorption spectroscopy (TDLAS) experiment platform was built based on quantum cascade laser with center wavelength at 1506.4 and 1340.2 cm−1. The concentration of SO2F2 and SOF2 is detected through this platform. And the concentration information of gas is deduced by detecting the voltage value of second harmonic signal. The experimental results show that, for SO2F2 gas between 1.01 and 19.88 ppm, the <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> is 0.989 and the maximum relative error is 2.044%. For SOF2 gas between 1.03 and 9.00 ppm, the <inline-formula> <tex-math notation="LaTeX">${R}^{{2}}$ </tex-math></inline-formula> is 0.992 and the maximum relative error is 0.728%. SO2 gas may cause weak interference to SOF2 detection, which has some regularities, and the interference can be eliminated by selecting the appropriate SOF2 fitting linear equation. The research results provide a feasible scheme for the quantitative detection of SO2F2 and SOF2 gases.

Infrared Spectrum Analysis and Quantitative Detection of SF6 Characteristic Decomposition Components SO2F2 and SOF2

Reverse modeling is a kind of technology that transforms natural scenes into three-dimensional models. The models are generated manually by referring to the point cloud obtained by a laser scanner, which is time-consuming, laborious, and complicated. To address this problem, we propose an automatic modeling method for the point cloud data of substations. First, an algorithm is designed to automatically generate a component model library by referring to the standard structure of the substation equipment. Then, we improve the Euclidean clustering to segment disconnected point cloud data. Finally, corresponding points are found according to the SHOT feature descriptor, and each component is identified with Hough voting. After the location information for each element in the scene is obtained, the models can be transferred to the scene to replace the corresponding part in the point cloud, thus completing the process of automatic modeling. The experiment compares the results of improved Euclidean clustering and traditional Euclidean clustering. The clustering method in this paper has a significant improvement in execution efficiency. In addition, we also give the final modeling result of the method in this paper. Compared with the Delaunay triangulation and Poisson surface reconstruction methods, the model built by this method is more complete.

Study on the automatic modeling method of 3D information model for substations

3D point cloud semantic segmentation is one of the key technologies for fast 3D modeling of digital twins. To address the overfitting problem of the large-scale semantic segmentation model RandLA-Net, this paper proposes an improved RandLA-Net method based on Mix3D data augmentation. RandLA-Net is a high-volume, large-perception field model that can directly capture the contextual information of the entire 3D scene, while 3D datasets tend to be more expensive due to data sampling and labeling, often the number of scenes is small and the variance within the data is small, RandLA-Net can easily learn the overly strong contextual prior on the training set, and the model may show poor generalization ability when reasoning in realistic scenes. By introducing Mix3D to mix the two scenes to generate new training samples and implicitly place the object instances in the new contextual environment, the RandLA-Net model no longer relies solely on the scene context to infer semantic labels, but instead infer from the local structure, balancing the role of global context and local structure information in model inference and effectively reducing the overfitting of the training set context. The overfitting of the training set context is effectively reduced. Experimental results on several datasets show that our approach results in a 1.3% and 0.6% mIoU improvement of the RandLA-Net model on Semantic3D and S3DIS datasets.

Mix3D data augmentation enhanced RandLA-Net for large-scale point cloud semantic segmentation

Power system is one of the most important infrastructures in modern society. In the power system, various sensitive information such as power supply status, load data and fault information need to be protected. In recent years, the methods based on deep learning has been widely used in the identification and protection of sensitive information in power systems. We propose a convolution neural network model based on pre-trained model and attention mechanism to classify and label power system data. Convolution neural network is a deep learning model, which offers a powerful and flexible tool for electric sensitive information detection. Pre-trained model and attention mechanism are two common technical means, which can improve the feature extraction and generalization ability of the model, thus providing effective support for image classification, target detection and other tasks. In the training process, the model we proposed realizes accurate and automatic recognition of sensitive information by learning the characteristics of input text information.

Sensitive Information Identification Method of Power System Based on Deep Learning

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