arXiv subjects

Sungjoo Chung

Publications and source records attributed to Sungjoo Chung.

4 recordsLinked to original sources

arXiv2026

Network Interdependency-Informed Power System Dynamic Trajectory Prediction Utilizing Black-Box Modeling of Multiple Inverter-Based Resources

Black-box modeling of inverter-based resources (IBRs) has attracted growing interest for real-time grid operation and control in the presence of proprietary electronic control architectures. Existing machine learning (ML)-based online dynamic trajectory prediction approaches using IBR black-box models either significantly accumulate prediction errors when multiple surrogates are simultaneously used or ignore measurement errors, limiting their deployment in practical grids. To address these limitations, this paper proposes a novel network interdependency-informed ML algorithm for online dynamic trajectory prediction in IBR-integrated power systems. A modular spatiotemporal attention network (STAN)-based predictor for the black-box modeling of each IBR unit is first proposed. Utilizing past measurements, the proposed STAN can effectively capture and predict the spatiotemporal dynamics of IBRs by employing an attention mechanism to attend to the most pertinent features for trajectory prediction. Furthermore, a novel hybrid physics-informed loss function that integrates a decoupled linearized AC power flow formulation is proposed. The proposed loss function effectively ensures physical consistency of predictions within network operation while avoiding the computational complexity of iterative power flow solving, thereby enabling efficient gradient backpropagation and overall improved prediction accuracy. Case studies on the IEEE 14- and WECC 179-bus systems demonstrate that the proposed method achieves significant accuracy enhancement and robustness against measurement errors, outperforming recent ML-based trajectory prediction methods.

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arXiv2026

Utilizing Adversarial Training for Robust Voltage Control: An Adaptive Deep Reinforcement Learning Method

Adversarial training is a defense method that trains machine learning models on intentionally perturbed attack inputs, so they learn to be robust against adversarial examples. This paper develops a robust voltage control framework for distribution networks with high penetration of distributed energy resources (DERs). Conventional voltage control methods are vulnerable to strategic cyber attacks, as they typically consider only random or black-box perturbations. To address this, we formulate white-box adversarial attacks using Projected Gradient Descent (PGD) and train a deep reinforcement learning (DRL) agent adversarially. The resulting policy adapts in real time to high-impact, strategically optimized perturbations. Simulations on DER-rich networks show that the approach maintains voltage stability and operational efficiency under realistic attack scenarios, highlighting the effectiveness of gradient-based adversarial DRL in enhancing robustness and adaptability in modern distribution system control.

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arXiv2024

Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method

Traditional power flow methods often adopt certain assumptions designed for passive balanced distribution systems, thus lacking practicality for unbalanced operation. Moreover, their computation accuracy and efficiency are heavily subject to unknown errors and bad data in measurements or prediction data of distributed energy resources (DERs). To address these issues, this paper proposes a hybrid data-aided robust power flow algorithm in unbalanced distribution systems, which combines Taylor series expansion knowledge with a data-driven regression technique. The proposed method initiates a linearization power flow model to derive an explicitly analytical solution by modified Taylor expansion. To mitigate the approximation loss that surges due to the DER integration and bad data, we further develop a data-aided robust support vector regression approach to estimate the errors efficiently. Comparative analysis in the 13-bus and 123-bus IEEE unbalanced feeders shows that the proposed algorithm achieves superior computational efficiency, with guaranteed accuracy and robustness against outliers.

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arXiv2023

Deep Reinforcement Learning-Enabled Adaptive Forecasting-Aided State Estimation in Distribution Systems with Multi-Source Multi-Rate Data

Distribution system state estimation (DSSE) is paramount for effective state monitoring and control. However, stochastic outputs of renewables and asynchronous streaming of multi-rate measurements in practical systems largely degrade the estimation performance. This paper proposes a deep reinforcement learning (DRL)-enabled adaptive DSSE algorithm in unbalanced distribution systems, which tackles hybrid measurements with different time scales efficiently. We construct a three-step forecasting-aided state estimation framework, including DRL-based parameter identification, prediction, and state estimation, with multi-rate measurements incorporating limited synchrophasor data. Furthermore, a DRL-based adaptive parameter identification mechanism is embedded in the prediction step. As a novel attempt at utilizing DRL to enable DSSE adaptive to varying operating conditions, this method improves the prediction performance and further facilitates accurate state estimation. Case studies in two unbalanced feeders indicate that our method captures state variation with multi-source multi-rate data efficiently, outperforming the traditional methods.

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