Research on Fault Location of Distribution Lines Based on the Standing Wave Principle

Aiming at the fast and accurate location of a single-phase ground fault in the distribution network, a single terminal injection signal location method, based on the standing wave principle, is proposed. Firstly, the double conductor standing wave principle formation, based on uniform transmission line theory, is analyzed, and the mathematical model of the fault distance algorithm is established. Secondly, a fault detection circuit is built by simulation, and the distribution trend of the standing wave and its relationship with unit capacitance and unit inductance are studied. By setting the source signal frequency and detection point interval and other parameters, the fault location of this method under direct grounding fault and through grounding resistance fault is simulated and studied. Finally, the fault distance is calculated and located by an experiment. The results show that the positioning accuracy is high, which verifies the effectiveness of the standing wave positioning method

Optimal Scheduling for Hybrid Battery Swapping System of Electric Vehicles

Range anxiety seriously restricts the development of electric vehicles (EVs). To address the above issue, a hybrid battery swapping system (HBSS) is developed in this paper. In the system, EVs can swap their battery at battery swapping stations or by the roadside via battery swapping vans. The proposed scheduling strategy aims to achieve the best service quality for the HBSS by controlling the mobile swapping service fee. In the model, the uncertainty of EV selection is managed by leveraging the Sigmoid function. Based on proving the uniqueness of the solution, the particle swarm optimization algorithm is used to solve the problem. Simulations validate the effectiveness of the proposed strategy in alleviating range anxiety. Moreover, the impacts of maximum service capacity and the operating rule have been analyzed

Fault line selection algorithm for distribution networks based on AdapGL‐GIN network

Abstract When a grounding fault occurs in the distribution network with distributed generation, the network topology becomes intricate, making it challenging to extract fault characteristics, resulting in a decrease in the precision of fault discrimination. To address this issue, a graph isomorphism network (GIN) approach based on the parameterized adaptive graph learning (AdapGL) module is proposed, transforming the distribution network fault selection problem into a graph classification task. First, the adjacency matrix of the distribution network's topology graph is initialized. This matrix, combined with feature vectors from the robust local mean decomposition energy entropy and transient dielectric loss angle of line, will be input into the GIN. Then, the AdapGL module is integrated into the GIN, dynamically learning and updating the one‐way relationships between actual network nodes to complete the graph classification task. Finally, a radial distribution network model (RDNM) and an improved IEEE 34 nodes model are established, and the fault selection results of the AdapGL‐GIN method are compared with those of other methods. The results indicate that the proposed method achieves higher accuracy than other methods, demonstrating significant practical importance in engineering applications

Obtuse Angle Prediction and Factor Evaluation for Image Reversible Data Hiding

A pixel-based pixel-value-ordering (PPVO) has been used for reversible data hiding to generate large embedding capacity and high-fidelity marked images. The original PPVO invented an effective prediction strategy in pixel-by-pixel manner. This paper extends PPVO and proposes an obtuse angle prediction (OAP) scheme, in which each pixel is predicted by context pixels with better distribution. Moreover, for evaluating prediction power, a mathematical model is constructed and three factors, including the context vector dimension, the maximum prediction angle, and the current pixel location, are analyzed in detail. Experimental results declare that the proposed OAP approach can achieve higher PSNR values than PPVO and some other state-of-the-art methods, especially in the moderate and large payload sizes

Ultra-Short-Term Load Forecasting for Customer-Level Integrated Energy Systems Based on Composite VTDS Models

A method is proposed to address the challenging issue of load prediction in user-level integrated energy systems (IESs) using a composite VTDS model. Firstly, an IES multi-dimensional load time series is decomposed into multiple intrinsic mode functions (IMFs) using variational mode decomposition (VMD). Then, each IMF, along with other influential features, is subjected to data dimensionality reduction and clustering denoising using t-distributed stochastic neighbor embedding (t-SNE) and fast density-based spatial clustering of applications with noise (FDBSCAN) to perform major feature selection. Subsequently, the reduced and denoised data are reconstructed, and a time-aware long short-term memory (T-LSTM) artificial neural network is employed to fill in missing data by incorporating time interval information. Finally, the selected multi-factor load time series is used as input into a support vector regression (SVR) model optimized using the quantum particle swarm optimization (QPSO) algorithm for load prediction. Using measured load data from a specific user-level IES at the Tempe campus of Arizona State University, USA, as a case study, a comparative analysis between the VTDS method and other approaches is conducted. The results demonstrate that the method proposed in this study achieved higher accuracy in short-term forecasting of the IES’s multiple loads

Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO

Most pixel-value-ordering (PVO) predictors generated prediction-errors including −1 and 1 in a block-by-block manner. Pixel-based PVO (PPVO) method provided a novel pixel scan strategy in a pixel-by-pixel way. Prediction-error bin 0 is expanded for embedding with the help of equalizing context pixels for prediction. In this paper, a PPVO-based hybrid predictor (HPPVO) is proposed as an extension. HPPVO predicts pixel in both positive and negative orientations. Assisted by expansion bins selection technique, this hybrid predictor presents an optimized prediction-error expansion strategy including bin 0. Furthermore, a novel field-biased context pixel selection is already developed, with which detailed correlations of around pixels are better exploited more than equalizing scheme merely. Experiment results show that the proposed HPPVO improves embedding capacity and enhances marked image fidelity. It also outperforms some other state-of-the-art methods of reversible data hiding, especially for moderate and large payloads

Fault Recovery Strategy for Power–Communication Coupled Distribution Network Considering Uncertainty

In the face of multiple failures caused by extreme disasters, the power and communication sides of the distribution network are interdependent in the fault recovery process. To improve the post-disaster recovery efficiency of the distribution network, this paper proposes a coordinated optimization strategy for distribution network reconfiguration and repair, which integrates the power and communication aspects. First, the recovery process is divided into islanding–reconfiguration and dynamic emergency repair. The coupling relationship between power and communication is considered; that is, power failure may cause communication nodes to lose power, and communication failure may affect the effective operation of remote control devices. Based on this, the fault recovery process is optimized with the objective of maximizing load transfer and direct recovery while introducing a stochastic model predictive control method to handle the uncertainty of distributed power generation by rolling optimization of typical scenarios. Finally, the effectiveness of the proposed strategy is verified using an improved IEEE33-node distribution network system. The simulation results show that the proposed method can recover power to the maximum extent and reduce loss while ensuring the safe and stable operation of the distribution system

Comparative Analysis Reveals the Metabolic Characteristics of Astringent Seeds of Chinese Fir (Cunninghamia lanceolata (Lamb) Hook) during Astringent Compounds Accumulation Stages

Research Highlights: The present study firstly reported the metabolic dynamics of astringent seed, a special type of abortion in Chinese fir, during the astringent material stages. The results provide a reference for further study on its occurrence mechanism and enrich the understanding of the plant seed developmental physiology. Background and Objectives: Astringent seed is a type of abortive phenomenon in Chinese fir, which significantly reduces the yield and quality of elite seeds for its high-incidence and indistinguishableness in seed orchard. Embryo defects can be observed in the astringent seed, accompanied with rapid accumulation of secondary metabolites. However, types of those metabolites in astringent seed, dynamic changes during seed growth process, and different accumulative characteristics compared to germinable seed have not been explained. Materials and Methods: Astringent and germinable seed samples were collected at four stages aim to determine the differences in their metabolic patterns. A liquid chromatography-mass spectrometry (LC-MS) detection was used to generate the raw metabolic peaks. Bioinformatics statistical strategies were used to further investigation. Results: A total of 421 metabolites were screened and 112 metabolites were identified as the different expressive metabolites including 68 up-regulated and 44 down-regulated metabolites. Those different expressive metabolites were grouped into 26 classes. Flavone, flavonol, and amino acid derivatives compounds were the most varied metabolites. Four subcategories which could represent the diverse basic expressive patterns or accumulative activity in different sample groups were further clustered. Moreover, pathways related to biosynthesis/degradation/metabolism of flavonoid-like compounds, amino acid/nucleotides derivatives, zeatin, and IAA were clearly enriched. Conclusions: Significant metabolic differences were observed across and between astringent and germinable seeds 105 d after pollination. Massive accumulation of flavonoids-like compounds, significant reduction of amino acids/nucleotides and their derivatives, and the abnormal expression of phytohormones, lipids and other secondary metabolites are the main metabolic characteristics in astringent seeds