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1. 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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2. Research Trends and Applications of PMUs

   https://doi.org/10.3390/en15155329  

   Paramo, Gian; Bretas, Arturo; Meyn, Sean (August 2022, Energies)

   This work is a survey of current trends in applications of PMUs. PMUs have the potential to solve major problems in the areas of power system estimation, protection, and stability. A variety of methods are being used for these purposes, including statistical techniques, mathematical transformations, probability, and AI. The results produced by the techniques reviewed in this work are promising, but there is work to be performed in the context of implementation and standardization. As the smart grid initiative continues to advance, the number of intelligent devices monitoring the power grid continues to increase. PMUs are at the center of this initiative, and as a result, each year more PMUs are deployed across the grid. Since their introduction, myriad solutions based on PMU-technology have been suggested. The high sampling rates and synchronized measurements provided by PMUs are expected to drive significant advancements across multiple fields, such as the protection, estimation, and control of the power grid. This work offers a review of contemporary research trends and applications of PMU technology. Most solutions presented in this work were published in the last five years, and techniques showing potential for significant impact are highlighted in greater detail. Being a relatively new technology, there are several issues that must be addressed before PMU-based solutions can be successfully implemented. This survey found that key areas where improvements are needed include the establishment of PMU-observability, data processing algorithms, the handling of heterogeneous sampling rates, and the minimization of the investment in infrastructure for PMU communication. Solutions based on Bayesian estimation, as well as those having a distributed architectures, show great promise. The material presented in this document is tailored to both new researchers entering this field and experienced researchers wishing to become acquainted with emerging trends. 

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3. Discrete Time Monitoring of Power Grids

   https://doi.org/10.1109/NAPS58826.2023.10318780  

   Acilan, Etki; Abur, Ali (October 2023, Proceedings of the Annual North American Power Symposium)

   Karakaya, Bora; Collins, Randy (Ed.)

   With high penetration of inverter-based renewable energy sources (IBRES) power system dynamics began to occur within shorter time frames. However, existing monitoring tools that rely on phasor measurements lack sufficient resolution to monitor such fast transients in the grid. To address this issue, this paper proposes a weighted least absolute value (WLAV) estimation-based monitoring tool that uses the same discrete samples of measured voltages and currents that are used by the phasor measurement units (PMU). Such samples are in general not made available to the users by the PMUs but instead they are processed for a full or fraction of a cycle to obtain the positive sequence phasors. Given that these discrete samples are readily available, there is little reason not to use them for tracking fast voltage and current transients introduced by inverter-based renewable energy sources. Performance of the proposed estimator is illustrated using a small IEEE 30-bus system modeled using appropriate discrete-time components and simulated measurements with Gaussian noise as well as gross errors. 

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4. Sub-Cycle Event Detection and Characterization in Continuous Streaming of Synchro-Waveforms: An Experiment Based on GridSweep Measurements

   https://doi.org/10.1109/NAPS58826.2023.10318763  

   Ehsani, Narges; Ahmadi-Gorjayi, Fatemeh; Ye, Zong-Jhen; McEachern, Alex; Mohsenian-Rad, Hamed (October 2023, IEEE)

   Continuous streaming of synchro-waveforms, i.e., time-synchronized waveform measurements, can provide a comprehensive record of the status of the power system. The key to unmask the value of such massive data recording is to extract the most informative aspects of the data. In this paper, we develop and test new methods to detect and characterize subcycle events in continuous streaming of synchro-waveforms. The measurements in this study are collected by the authors in a practical test-bed in California. The measurements are made at low-voltage circuits under two different substations, using GridSweep devices with GPS time stamping. Over 40 billion data points were collected during one month. Several practical challenges are addressed, including the computational complexity due to the enormous size of data, the need for realignment between waveform samples and cycles, and the challenges in extracting differential waveforms to reveal the event signatures. 

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5. Deep Neural Network-based State Estimator for Transmission System Considering Practical Implementation Challenges

   https://doi.org/10.35833/MPCE.2023.000997  

   Cheeramban_Varghese, Antos; Shah, Hritik; Azimian, Behrouz; Pal, Anamitra; Farantatos, Evangelos (January 2024, Journal of Modern Power Systems and Clean Energy)

   As the phasor measurement unit (PMU) placement problem involves a cost-benefit trade-off, more PMUs get placed on higher-voltage buses. However, this leads to the fact that many lower-voltage levels of the bulk power system cannot be observed by PMUs. This lack of visibility then makes time-synchronized state estimation of the full system a challenging problem. In this paper, a deep neural network-based state estimator (DeNSE) is proposed to solve this problem. The DeNSE employs a Bayesian framework to indirectly combine the inferences drawn from slow-timescale but widespread supervisory control and data acquisition (SCADA) data with fast-timescale but selected PMU data, to attain sub-second situational awareness of the full system. The practical utility of the DeNSE is demonstrated by considering topology change, non-Gaussian measurement noise, and detection and correction of bad data. The results obtained using the IEEE 118-bus system demonstrate the superiority of the DeNSE over a purely SCADA state estimator and a PMU-only linear state estimator from a techno-economic viability perspective. Lastly, the scalability of the DeNSE is proven by estimating the states of a large and realistic 2000-bus synthetic Texas system. 

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