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1. Localization of forced oscillations in the power grid under resonance conditions

   https://doi.org/10.1109/CISS.2018.8362302  

   Huang, Tong; Freris, Nikolaos M.; Kumar, P. R.; Xie, Le (March 2018, 52nd Annual Conference on Information Sciences and Systems (CISS))

   This paper proposes a data-driven method to pinpoint the source of a new emerging dynamical phenomenon in the power grid, referred to “forced oscillations” in the difficult but highly risky case where there is a resonance phenomenon. By exploiting the low-rank and sparse properties of synchrophasor measurements, the localization problem is formulated as a matrix decomposition problem, which can be efficiently solved by the exact augmented Lagrange multiplier algorithm. An online detection scheme is developed based on the problem formulation. The data-driven nature of the proposed method allows for a very efficient implementation. The efficacy of the proposed method is illustrated in a 68-bus power system. The proposed method may possibly be more broadly useful in other situations for identifying the source of forced oscillations in resonant systems. Index Terms—Forced oscillations, resonant systems, phasor measurement unit (PMU), robust principal component analysis (RPCA), Big Data. 

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2. Residual Saturation Based Kalman Filter for Smart Grid State Estimation Under Cyber Attacks

   Rana, Md Masud; Bo, Rui; Choi, Bong Jun (August 2019, Proceedings of 9th IEEE International Conference on CYBER Technology in Automation, Control and Intelligent Systems (IEEE-CYBER 2019))

   Most of the traditional state estimation algorithms are provided false alarm when there is attack. This paper proposes an attack-resilient algorithm where attack is automatically ignored, and the state estimation process is continuing which acts a grid-eye for monitoring whole power systems. After modeling the smart grid incorporating distributed energy resources, the smart sensors are deployed to gather measurement information where sensors are prone to attacks. Based on the noisy and cyber attack measurement information, the optimal state estimation algorithm is designed. When the attack is happened, the measurement residual error dynamic goes high and it can ignore using proposed saturation function. Moreover, the proposed saturation function is automatically computed in a dynamic way considering residual error and deigned parameters. Combing the aforementioned approaches, the Kalman filter algorithm is modified which is applied to the smart grid state estimation. The simulation results show that the proposed algorithm provides high estimation accuracy. 

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3. Vulnerability Assessment of Large-scale Power Systems to False Data Injection Attacks

   https://doi.org/10.1109/SmartGridComm47815.2020.9302956  

   Chu, Zhigang; Zhang, Jiazi; Kosut, Oliver; Sankar, Lalitha (November 2020, 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm))

   null (Ed.)

   This paper studies the vulnerability of large-scale power systems to false data injection (FDI) attacks through their physical consequences. An attacker-defender bi-level linear program (ADBLP) can be used to determine the worst-case consequences of FDI attacks aiming to maximize the physical power flow on a target line. This ADBLP can be transformed into a single-level mixed-integer linear program (MILP), but it is numerically intractable for power systems with a large number of buses and branches. In this paper, a modified Benders' decomposition algorithm is proposed to solve the ADBLP on large power systems without converting it to the MILP. Of more general interest, the proposed algorithm can be used to solve any ADBLP. Vulnerability of the IEEE 118-bus system and the Polish system with 2383 buses to FDI attacks is assessed using the proposed algorithm. 

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4. Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning

   https://doi.org/10.1109/TPWRS.2019.2919157  

   Mestav, Kursat Rasim; Luengo-Rozas, Jaime; Tong, Lang (January 2020, IEEE Transactions on Power Systems)

   The problem of state estimation for unobservable distribution systems is considered. A deep learning approach to Bayesian state estimation is proposed for real-time applications. The proposed technique consists of distribution learning of stochastic power injection, a Monte Carlo technique for the training of a deep neural network for state estimation, and a Bayesian bad-data detection and filtering algorithm. Structural characteristics of the deep neural networks are investigated. Simulations illustrate the accuracy of Bayesian state estimation for unobservable systems and demonstrate the benefit of employing a deep neural network. Numerical results show the robustness of Bayesian state estimation against modeling and estimation errors and the presence of bad and missing data. Comparing with pseudo-measurement techniques, direct Bayesian state estimation via deep learning neural network outperforms existing benchmarks. 

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5. Data-Driven Models for Sub-Cycle Dynamic Response of Inverter-Based Resources Using WMU Measurements

   https://doi.org/10.1109/TSG.2023.3280367  

   Ahmadi-Gorjayi, Fatemeh; Mohsenian-Rad, Hamed (September 2023, IEEE Transactions on Smart Grid)

   Using real-world data from Waveform Measurement Units (WMUs), this letter proposes novel data-driven methods to model the dynamic response of inverter-based resource (IBR) to the high-frequency disturbances that occur in practice in power systems. WMUs are an emerging class of smart grid sensors. They can capture the fast sub-cycle dynamics in power systems, which are overlooked by phasor measurement units (PMUs). After extracting the differential voltage and current waveforms from the raw waveform data, we develop multiple methods that include data-driven model library construction and proper model selection. One class of methods is proposed in frequency domain, which is based on modal analysis. Another class of methods is proposed in time domain, which is based on regression analysis of time-series. Experimental results based on real-world WMU data demonstrate the of performance the proposed methods. 

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