The paper presents a fault region identification method for the active distribution network (ADN) with limited PMU. First, PMU configuration and region division strategies are proposed based on the network topology. Next, the difference in three-phase current variations between the normal and fault regions of the ADN is analyzed. A multi-dimensional state monitoring matrix is built using the measured current data. The spectral norm ratio coefficient is constructed based on the 2-norm to lower the complexity of the multi-dimensional state monitoring matrix and quantify the difference in state changes before and after the fault occurs in each region. Then, the fault region is identified by combining each region's spectral norm ratio coefficient and the change of the current phase. Finally, an IEEE 33-node simulation model is created to simulate faults under different working conditions. According to the simulation results, the suggested approach is less impacted by fault type, neutral point grounding mode, and transition resistance. Furthermore, even if the communication does not match the rigorous synchronization requirements, the proposed method can still complete the fault identification of the distribution network correctly and has high robustness.
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http://dx.doi.org/10.1038/s41598-024-62859-6 | DOI Listing |
Entropy (Basel)
January 2025
Department of Electronics and Telecommunications (DET), Politecnico di Torino, 10129 Turin, Italy.
Quadratic forms with random kernel matrices are ubiquitous in applications of multivariate statistics, ranging from signal processing to time series analysis, biomedical systems design, wireless communications performance analysis, and other fields. Their statistical characterization is crucial to both design guideline formulation and efficient computation of performance indices. To this end, random matrix theory can be successfully exploited.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
Henan Key Laboratory of Imaging and Intelligent Processing, Information Engineering University, Zhengzhou, China.
Background: Photon-counting computed tomography (CT) is an advanced imaging technique that enables multi-energy imaging from a single scan. However, the limited photon count assigned to narrow energy bins leads to increased quantum noise in the reconstructed spectral images. To address this issue, leveraging the prior information in the spectral images is essential.
View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario, Canada M5S 3H6.
The cost of encoding a system Hamiltonian in a digital quantum computer as a linear combination of unitaries (LCU) grows with the 1-norm of the LCU expansion. The Block Invariant Symmetry Shift (BLISS) technique reduces this 1-norm by modifying the Hamiltonian action on only the undesired electron-number subspaces. Previously, BLISS required a computationally expensive nonlinear optimization that was not guaranteed to find the global minimum.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Mathematical Sciences, Harbin Engineering University, Harbin 150001, China.
Multi-view clustering has garnered significant attention due to its capacity to utilize information from multiple perspectives. The concept of anchor graph-based techniques was introduced to manage large-scale data better. However, current methods rely on K-means or uniform sampling to select anchors in the original space.
View Article and Find Full Text PDFNeural Netw
December 2024
The school of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China. Electronic address:
Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various domains, including the detection of emotions in patients with consciousness disorders, assisting in the diagnosis of depression, and assessing cognitive load. This process is critically important in the development and research of brain-computer interfaces, where precise and efficient recognition of emotions is paramount. In this work, we introduce a novel approach for emotion recognition employing multi-scale EEG features, denominated as the Dynamic Spatial-Spectral-Temporal Network (DSSTNet).
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