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Power System Security and Stability

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 1915

Special Issue Editors

Faculty of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA
Interests: smart grid; cybersecurity of smart grids; power system state estimation; data-driven cyberattacks; machine learning applications in smart grid
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Guest Editor
Department of Engineering and Industrial Professions, University of North Alabama, Florence, AL 35632, USA
Interests: renewable energy; condition monitoring of electrical apparatus; fault diagnosis and prognosis

Special Issue Information

Dear Colleagues,

The electric grid is undergoing significant changes due to the integration of clean energy resources, such as solar and wind, in an attempt to address the impacts of climate change. Distributed energy resources (DERs), such as wind and solar energy, are replacing traditional energy generation. However, this also brings about various threats of instabilities and security concerns in the forms of cyberattacks, voltage instability, and power quality disturbances. For example, the risk of cyberattacks on DERs rises, with the potential for a broader impact, as more solar and DER devices are connected to the grid. In addition, the high penetration level, dispersed location, and intermittent output of renewable energy bring security and stability issues to the operation and control of power systems. Advanced metering infrastructure, the SCADA system, the distributed energy resource management system, and artificial intelligence are effective systems and tools that can be utilized to ensure a reliable, stable, and secure grid operation. Thus, the objective of this Special Issue is to address the security and stability issues of power systems’ operation and control.

Dr. Bo Liu
Dr. Yayu Peng
Guest Editors

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Keywords

  • dynamic security assessment
  • voltage and frequency stability
  • detection, identification, and mitigation of cyberattacks against power systems
  • cybersecurity of DERs and inverter-based resources (IBRs)
  • renewable energy integration and impact
  • electric vehicle integration and impact
  • artificial intelligence techniques in power systems’ operation and control

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Published Papers (2 papers)

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Research

12 pages, 1909 KiB  
Article
A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD
by Jinggeng Gao, Honglei Xu, Yong Yang, Haoming Niu, Jinping Liang and Haiying Dong
Appl. Sci. 2024, 14(24), 11484; https://doi.org/10.3390/app142411484 - 10 Dec 2024
Viewed by 594
Abstract
In power systems, the application of wind power generation equipment and power electronic devices leads to an increased frequency of broadband oscillation events, and the detection of oscillation information becomes extremely difficult, due to the limitations of communication bandwidth and the sampling theorem. [...] Read more.
In power systems, the application of wind power generation equipment and power electronic devices leads to an increased frequency of broadband oscillation events, and the detection of oscillation information becomes extremely difficult, due to the limitations of communication bandwidth and the sampling theorem. To ensure the safety and stability of a power system, this paper presents a new recognition method of broadband oscillation information, which combines compressed sensing (CS) technology and an ensemble empirical mode decomposition (EEMD) algorithm to solve the problem of wideband oscillation recognition. Firstly, the broadband oscillation signal data collected by the phasor measuring unit (PMU) is compressed and sampled by a Gaussian random matrix in the substation, then the low-dimensional data obtained is uploaded to the main station. Secondly, in the main station, the subspace pursuit (SP) algorithm is used to reconstruct the low-dimensional signal; the broadband oscillation signal is recovered without losing the main features of the signal. Finally, we use the EEMD algorithm to decompose the reconstructed signal; the intrinsic mode function (IMF) components containing wideband oscillation information are screened by the energy coefficient, and the wideband oscillation information is identified. Full article
(This article belongs to the Special Issue Power System Security and Stability)
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13 pages, 377 KiB  
Article
Improved Subsynchronous Oscillation Parameter Identification Based on Eigensystem Realization Algorithm
by Gang Chen, Xueyang Zeng, Yilin Liu, Fang Zhang and Huabo Shi
Appl. Sci. 2024, 14(17), 7841; https://doi.org/10.3390/app14177841 - 4 Sep 2024
Viewed by 990
Abstract
Subsynchronous oscillation (SSO) is the resonance between a new energy generator set and a weak power grid, and the resonance frequency is usually the sub-/super-synchronous frequency. The eigensystem realization algorithm (ERA) is a classic algorithm for extracting modal parameters based on matrix decomposition. [...] Read more.
Subsynchronous oscillation (SSO) is the resonance between a new energy generator set and a weak power grid, and the resonance frequency is usually the sub-/super-synchronous frequency. The eigensystem realization algorithm (ERA) is a classic algorithm for extracting modal parameters based on matrix decomposition. By leveraging the ERA’s simplicity and low computational cost, an enhanced methodology for identifying the key parameters of SSO is introduced. The enhanced algorithm realizes SSO angular frequency extraction by constructing an angular frequency fitting equation, enabling efficient identification of SSO parameters using only a 200 ms synchrophasor sequence. In the process of identification, the fitting-based ERA effectively addresses the limitation of the existing ERA. The accuracy of SSO parameter identification is improved, thereby realizing that SSO parameter identification can be carried out using a 200 ms data window. The fitting-based ERA is verified using synthetic and actual data from synchrophasor measurement terminals. The research results show that the proposed algorithm can accurately extract fundamental and subsynchronous or supersynchronous oscillation parameters, effectively realizing dynamic real-time monitoring of subsynchronous oscillations. Full article
(This article belongs to the Special Issue Power System Security and Stability)
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