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  1. Free, publicly-accessible full text available January 1, 2024
  2. Free, publicly-accessible full text available November 1, 2023
  3. Free, publicly-accessible full text available November 1, 2023
  4. This paper describes how domain knowledge of power system operators can be integrated into reinforcement learning (RL) frameworks to effectively learn agents that control the grid's topology to prevent thermal cascading. Typical RL-based topology controllers fail to perform well due to the large search/optimization space. Here, we propose an actor-critic-based agent to address the problem's combinatorial nature and train the agent using the RL environment developed by RTE, the French TSO. To address the challenge of the large optimization space, a curriculum-based approach with reward tuning is incorporated into the training procedure by modifying the environment using network physics for enhanced agent learning. Further, a parallel training approach on multiple scenarios is employed to avoid biasing the agent to a few scenarios and make it robust to the natural variability in grid operations. Without these modifications to the training procedure, the RL agent failed for most test scenarios, illustrating the importance of properly integrating domain knowledge of physical systems for real-world RL learning. The agent was tested by RTE for the 2019 learning to run the power network challenge and was awarded the 2nd place in accuracy and 1st place in speed. The developed code is open-sourced for public use. Analysis of a simple system proves the enhancement in training RL-agents using the curriculum. 
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    Free, publicly-accessible full text available October 1, 2023
  5. The growing integration of distributed energy resources (DERs) in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors. With large-scale DER penetration in distribution grids, traditional outage detection methods, which rely on customers report and smart meters' “last gasp” signals, will have poor performance, because renewable generators and storage and the mesh structure in urban distribution grids can continue supplying power after line outages. To address these challenges, we propose a data-driven outage monitoring approach based on the stochastic time series analysis with a theoretical guarantee. Specifically, we prove via power flow analysis that dependency of time-series voltage measurements exhibits significant statistical changes after line outages. This makes the theory on optimal change-point detection suitable to identify line outages. However, existing change point detection methods require post-outage voltage distribution, which are unknown in distribution systems. Therefore, we design a maximum likelihood estimator to directly learn distribution pa-rameters from voltage data. We prove the estimated parameters-based detection also achieves optimal performance, making it extremely useful for fast distribution grid outage identifications. Furthermore, since smart meters have been widely installed in distribution grids and advanced infrastructure (e.g., PMU) has not widely been available, our approach only requires voltage magnitude for quick outage identification. Simulation results show highly accurate outage identification in eight distribution grids with 17 configurations with and without DERs using smart meter data. 
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  6. Due to limited amplitude and controlled phase of current supplied by inverter-interfaced renewable power plants (IIRPPs), the IIRPP-side distance protection of lines connected to IIRPPs fails to detect the fault location accurately, so it may malfunction. The composite sequence network of a line connected to an IIRPP during asymmetrical faults is analyzed, and an adaptive distance protection based on the analytical model of additional impedance is proposed in this study. Based on open circuit property of negative-sequence network at the IIRPP-side, the equivalent impedance of power grid and current flowing through fault point are calculated in real-time using local measurements, which are substituted into the analytical model of additional impedance to calculate fault location. In the case of negative-sequence reactive current injection from IIRPPs during asymmetrical faults, the error of calculating fault point current from local measurements is analyzed and corrected to ensure reliability of the proposed protection. The proposed protection alleviates the effect of fault resistance in a system with weak sources. In addition, the proposed protection can adapt to different grid codes (GCs), the operation mode change of the power grid, and the capacity change of the IIRPP. PSCAD/EMTDC test results verify the effectiveness of the proposed protection. 
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