Shuangteng Han, Xinwei Sun, Yuhong Wang, Zongsheng Zheng, Xi Wang, Peng Shi, Yunxiang Shi, Yao He
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A method for searching splitting surface considering network splitting adaptation index
As an effective control measure to ensure uninterrupted power supply to critical loads under extreme faults, network splitting is of great significance for maintaining system safety and stability. The purpose of this study is to develop a method to accurately and quickly find a reasonable splitting surface and reliably perform network splitting. To address the current problem of poor node classification when splitting, the correlation between nodes is obtained through modal analysis of the system. Node classification criteria are proposed to accurately classify different types of nodes and obtain a suitable splitting space. Based on the node correlation, a splitting adaptation index reflecting the suitability of splitting is proposed. Furthermore, a comprehensive index for the optimisation of the splitting surface is proposed by combining the minimum unbalanced power and the splitting adaptation index, and the splitting surface is quickly determined based on this index. Finally, simulation verification is carried out using the IEEE-118 standard system, which shows that the method can accurately determine the splitting space and optimise the selection of the splitting surface.
期刊介绍:
IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more.
Topics covered by scope include, but are not limited to:
advances in single and multi-dimensional filter design and implementation
linear and nonlinear, fixed and adaptive digital filters and multirate filter banks
statistical signal processing techniques and analysis
classical, parametric and higher order spectral analysis
signal transformation and compression techniques, including time-frequency analysis
system modelling and adaptive identification techniques
machine learning based approaches to signal processing
Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques
theory and application of blind and semi-blind signal separation techniques
signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals
direction-finding and beamforming techniques for audio and electromagnetic signals
analysis techniques for biomedical signals
baseband signal processing techniques for transmission and reception of communication signals
signal processing techniques for data hiding and audio watermarking
sparse signal processing and compressive sensing
Special Issue Call for Papers:
Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf