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   Ranhee Yoon, Erdi Gulbahce (October 2023, MECC 2023)
2. Free Vibration Modeling of Power Lines Conductors

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3. Robust Scale-Free Synthesis for Frequency Control in Power Systems

   https://doi.org/10.1109/TCNS.2019.2922503  

   Pates, Richard; Mallada, Enrique (June 2019, IEEE Transactions on Control of Network Systems)

   The AC frequency in electrical power systems is conventionally regulated by synchronous machines. The gradual replacement of these machines by asynchronous renewable-based generation, which provides] little or no frequency control, increases system uncertainty and risk of instability. This poses hard limits on the proportion of renewables that can be integrated into the system. To address this issue, in this paper, we develop a framework for performing frequency control in power systems with arbitrary mixes of conventional and renewable generation. Our approach is based on a robust stability criterion that can be used to guarantee the stability of a full power system model based on a set of decentralised tests, one for each component in the system. It can be applied even when using detailed heterogeneous component models and can be verified using several standard frequency response, state-space, and circuit theoretic analysis tools. By designing decentralised controllers for individual components to meet these decentralised tests, strong apriori robust stability guarantees, that hold independently of the operating point and remain valid even as components are added to and removed from the grid, can be given. This allows every component to contribute to the regulation of system frequency in a simple and provable manner. Notably, our framework certifies the stability of several existing (non-passive) power system control schemes and models, and allows for the study of robustness with respect to delays. 

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4. A Derivative-Free Observability Analysis Method of Stochastic Power Systems

   https://doi.org/10.1109/PESGM46819.2021.9637959  

   Zheng, Zongsheng; Xu, Yijun; Mili, Lamine; Liu, Zhigang; Korkali, Mert; Wang, Yuhong (July 2021, 2021 IEEE Power & Energy Society General Meeting (PESGM))

   The observability analysis of a time-varying nonlinear dynamic model has recently attracted the attention of power engineers due to its vital role in power system dynamic state estimation. Generally speaking, due to the nonlinearity of the power system dynamic model, the traditional derivative-based observability analysis approaches either rely on the linear approximation to simplify the problem or require a complicated derivation procedure that ignores the uncertainties of the dynamic system model and of the observations represented by stochastic noises. Facing this challenge, we propose a novel polynomial-chaos-based derivative-free observability analysis approach that not only brings a low complexity, but also enables us to quantify the degree of observability by considering the stochastic nature of the dynamic systems. The excellent performances of the proposed method is demonstrated using simulations of a decentralized dynamic state estimation performed on a power system using a synchronous generator model with IEEE-DC1A exciter and a TGOV1 turbine-governor. 

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5. Model-free Data Authentication for Cyber Security in Power Systems

   https://doi.org/10.1109/TSG.2020.2986704  

   Liu, Shengyuan; You, Shutang; Yin, He; Lin, Zhenzhi; Liu, Yilu; Yao, Wenxuan; Sundaresh, Lakshmi (April 2020, IEEE Transactions on Smart Grid)

   With the development and wide deployment of measurement equipment, data can be automatically measured and visualized for situation awareness in power systems. However, the cyber security of power systems is also threatened by data spoofing attacks. This letter proposed a measurement data source authentication (MDSA) algorithm based on feature extraction techniques including ensemble empirical mode decomposition (EEMD) and fast Fourier transform (FFT), and machine learning for real-time measurement data classification. Compared with previous work, the proposed algorithm can achieve higher accuracy of MDSA using a shorter window of data from closely located synchrophasor measurement sensors. 

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