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Energy storage ability is determined by the ion arrangement and electron transfer at the electrode–electrolyte interface, which depends on combining materials that form complex heterogeneous interf...

Covalent organic frameworks (COFs) have developed as a precisely tailor-made platform for integrating different functionalities into the frameworks via reticular chemistry. In this work, two COFs w...

Developing highly efficient and durable electrocatalysts for the oxygen reduction reaction (ORR) is essential for commercializing renewable and clean energy, but this remains a challenge. Herein, w...

Ni1/3Co2/3(CO3)1/2(OH)·0.11H2O (NCC) was in situ constructed on the surface of CNTs, which realized the nanoscale recombination of NCC and CNTs. The as-obtained CNTs@NCC composite showed excellent electrochemical performance in both aqueous and all-solid-state electrolyte systems owing to the improvement in the utilization of active materials. This work provides a general duty strategy for improving the combined electrochemical performance of electrode materials for energy storage.

Porous and carbon composited binary transition metal oxide nanomaterials provide high ionic conductivity and electronic conductivity. However, it still remains a challenge to use a simple method to achieve in situ both composite and porous one-dimensional (1D) nanorods. In this paper, we have prepared porous NiMoO4/C (NMO/C) composite nanorods using a solvothermal method and subsequent annealing step. In the solvothermal reaction, a simple organic solvent, cyclohexane, was introduced as a carbon source and pore-forming agent, realizing the controllable synthesis of porous 1D nanorods composited with heteroatoms via a simple strategy. This structure shows improved ionic and electronic conductivities and provides a large number of electrochemical reaction active sites, making it a possible candidate to be used as a high capacity electrode material for supercapacitors. On this basis, electrode materials with higher rate performance and utilization were prepared by mixing NMO/C with carbon nanotubes (CNTs) through simple physical mixing. Due to the 1D electrochemical synergistic effects of NMO/C and CNTs, the assembled NMO/C-CNT electrode shows a specific capacity of 325.1 F g−1 (at a current density of 0.5 A g−1) in a two-electrode system. At a high current density of 8 A g−1, the specific capacity is still 182 F g−1. This report provides a new strategy for the designed in situ synthesis of porous composite nanorods.

With the development of big data and deep learning algorithms, conventional fault analysis and diagnosis methods is gradually replaced by data-driven methods. When applying data-driven methods into transmission line fault classification, it’s liable to associate with Convolutional Neural Network because of its success in pattern recognition. Considering fault recorded signal’s temporal property, we propose an approach using raw recorded data based on onedimensional convolutional neural network (CNN-ID). In this paper, simulation samples are used for network training and adjustment, sparing actual samples for final evaluation. We also unify input data size by spline interpolation so that the fixed model can fit actual samples of different sampling frequency. Experimental results show that the proposed method can be conveniently used and achieve high classification accuracy fast with low sampling frequency, which is not affected by voltage level, fault distance, fault inception angles, fault resistance and so on. CNN-ID shows high generalization and robustness in transmission line fault classification and thus has great potential to apply in the construction of modern intelligent diagnosis system.

This paper proposes a Low Frequency Oscillation (LFO) parameters estimation scheme based on Bacterial Swarm Algorithm (BSA). LFO is caused by a wide variety of events, including system faults and load switching. Thus, it is an important task to accurately estimate the parameters of LFO, and further perform fault diagnosis. Although the techniques such as Prony’s method could reconstruct the form of the signal, the estimated parameters are not accurate enough. In order to improve the estimation accuracy, this research improves the regression objective function, which aims to minimize the probability likelihood between a segment of the signal filtered by a Mathematica Morphology (MM) filter and its regression. In the experimental studies, BSA is used to optimize the proposed objective function. Comprehensive comparisons are taken among the proposed method, other Evolutionary Algorithms (EAs), and conventional signal processing techniques, which show BSA with Local Probability likelihood (BSA-LP) has the best performance on the estimation.

Establishing indicators for evaluating and forecasting the overall defect status of transmission lines are of great significance for the operation and maintenance, while studying the impact of different external factors on the development process of transmission line defects is also important. Therefore, this paper proposes a transmission line defect risk model and a SVM based prediction method. By dividing the transmission line into several component according to its own structure, the definition of the defect risk index is given and used to evaluate the status of the transmission line defects, which is based on the defect severity quantification and the membership degree analysis of each component. The historical defect risk value samples are calculated based on the historical defect data, and then use the Pearson correlation coefficient to select the meteorological factors with large impact on the defect development process to each component of the transmission line. Construct training samples with related factors, build a RBFSVM based transmission line defect risk value prediction model, and predict the defect risk value of the transmission line in the future. An example analysis is conducted on the transmission lines of Guangdong power grid to predict the defect risk value of a transmission line in each month of 2018. The results show the feasibility and accuracy of the proposed model and forecasting method in this paper.

Improving the self-conductivity and structural stability of electrode materials is a key strategy to improve the energy density, rate performance, and cycle life of supercapacitors. Controlled intercalation of ethyl carbamate (CH