一种考虑网络分裂自适应指标的分裂曲面搜索方法

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Shuangteng Han, Xinwei Sun, Yuhong Wang, Zongsheng Zheng, Xi Wang, Peng Shi, Yunxiang Shi, Yao He
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引用次数: 0

摘要

网络分裂作为保证极端故障下关键负荷不间断供电的有效控制措施,对维护系统安全稳定具有重要意义。本研究的目的是开发一种准确快速地找到合理分裂表面并可靠地进行网络分裂的方法。为了解决当前拆分时节点分类不好的问题,通过对系统的模态分析来获得节点之间的相关性。提出了节点分类准则,以准确地对不同类型的节点进行分类,并获得合适的划分空间。基于节点相关性,提出了一种反映分裂适用性的分裂自适应指标。此外,通过将最小不平衡功率和分裂适应指数相结合,提出了分裂表面优化的综合指数,并基于该指数快速确定分裂表面。最后,利用IEEE-118标准系统进行了仿真验证,表明该方法可以准确地确定分裂空间,优化分裂表面的选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A method for searching splitting surface considering network splitting adaptation index

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.

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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
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
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