@article{mukherjee_chakrabortty_babaei_2021, title={Modeling and Quantifying the Impact of Wind Penetration on Slow Coherency of Power Systems}, volume={36}, ISSN={["1558-0679"]}, DOI={10.1109/TPWRS.2020.3022832}, abstractNote={This paper presents a mathematical analysis of how wind generation impacts the slow coherency property of power systems. Slow coherency arises from time-scale separation in the dynamics of synchronous generators, where generator states inside a coherent area synchronize over a fast time-scale due to stronger coupling, while the areas themselves synchronize over a slower time-scale due to weaker coupling. This time-scale separation is reflected in the form of a spectral separation in the weighted Laplacian matrix describing the swing dynamics of the generators. However, when wind farms with doubly-fed induction generators (DFIG) are integrated into the system then this Laplacian matrix changes based on both the level of wind penetration and the location of the wind farms. The modified Laplacian changes the effective slow eigenspace of the generators. Depending on the penetration level, this change may result in changing the identities of the coherent areas. We develop a theoretical framework to quantify this modification, and propose an equivalent Laplacian matrix to compute the modified coherent areas. Results are validated using the IEEE 68-bus test system with one and multiple wind farms. The model-based slow coherency results are compared with measurement-based principal component analysis to substantiate our derivations.}, number={2}, journal={IEEE TRANSACTIONS ON POWER SYSTEMS}, author={Mukherjee, Sayak and Chakrabortty, Aranya and Babaei, Saman}, year={2021}, month={Mar}, pages={1002–1012} }
 @article{mukherjee_bai_chakrabortty_2021, title={Reduced-dimensional reinforcement learning control using singular perturbation approximations}, volume={126}, ISSN={["1873-2836"]}, DOI={10.1016/j.automatica.2020.109451}, abstractNote={We present a set of model-free, reduced-dimensional reinforcement learning (RL) based optimal control designs for linear time-invariant singularly perturbed (SP) systems. We first present a state-feedback and output-feedback based RL control design for a generic SP system with unknown state and input matrices. We take advantage of the underlying time-scale separation property of the plant to learn a linear quadratic regulator (LQR) for only its slow dynamics, thereby saving a significant amount of learning time compared to the conventional full-dimensional RL controller. We analyze the sub-optimality of the design using SP approximation theorems and provide sufficient conditions for closed-loop stability. Thereafter, we extend both designs to clustered multi-agent consensus networks, where the SP property reflects through clustering. We develop both centralized and cluster-wise block-decentralized RL controllers for such networks, in reduced dimensions. We demonstrate the details of the implementation of these controllers using simulations of relevant numerical examples and compare them with conventional RL designs to show the computational benefits of our approach.}, journal={AUTOMATICA}, author={Mukherjee, Sayak and Bai, He and Chakrabortty, Aranya}, year={2021}, month={Apr} }
 @article{mukherjee_chakrabortty_bai_darvishi_fardanesh_2021, title={Scalable Designs for Reinforcement Learning-Based Wide-Area Damping Control}, volume={12}, ISSN={["1949-3061"]}, DOI={10.1109/TSG.2021.3050419}, abstractNote={This article discusses how techniques from reinforcement learning (RL) can be exploited to transition to a model-free and scalable wide-area oscillation damping control of power grids. We present two control architectures with distinct features. Performing full-dimensional RL control designs for any practical grid would require an unacceptably long learning time and result in a dense communication architecture. Our designs avoid the curse of dimensionality by employing ideas from model reduction. The first design exploits time-scale separation in the generator electro-mechanical dynamics arising from coherent clustering, and learns a controller using both electro-mechanical and non-electro-mechanical states while compensating for the error in incorporating the latter through the RL loop. The second design presents an output-feedback approach enabled by a neuro-adaptive observer using measurements of only the generator frequencies. The controller exhibits an adaptive behavior that updates the control gains whenever there is a notable change in the loads. Theoretical guarantees for closed-loop stability and performance are provided for both designs. Numerical simulations are shown for the IEEE 68-bus power system model.}, number={3}, journal={IEEE TRANSACTIONS ON SMART GRID}, author={Mukherjee, Sayak and Chakrabortty, Aranya and Bai, He and Darvishi, Atena and Fardanesh, Bruce}, year={2021}, month={May}, pages={2389–2401} }
 @article{mukherjee_babaei_chakrabortty_fardanesh_2020, title={Measurement-driven optimal control of utility-scale power systems: A New York State grid perspective}, volume={115}, ISSN={["1879-3517"]}, DOI={10.1016/j.ijepes.2019.105470}, abstractNote={This paper focuses on designing and testing a supplementary controller for an ultra-large, utility-scale power system, namely the New York State (NYS) Power Grid from a completely measurement-based perspective. We present the control design using the Flexible AC Transmission System (FACTS) facility at the NYS grid. We use the utility-scale Eastern Interconnection (EI) model consisting of over 70,000 buses in the PSS/E platform for this research. The coherency structure of the NYS grid is analyzed by performing Principal Component Analysis (PCA) on frequency measurements obtained from multiple contingency simulations. Thereafter, we use the frequency measurements from PMU-enabled buses to identify a reduced-order state space model of the grid such that it matches the input-output characteristics along with identifying the inter-area modes. This model is then used to design the Linear Quadratic Gaussian (LQG) based optimal FACTS controller. Then the controller is implemented in the PSS/E model of the Eastern Interconnection (EI) as a PSS/E-FORTRAN based user defined module. The effectiveness of the control performance is shown using the non-linear simulations under different contingencies provided by the New York Independent System Operator (NYISO).}, journal={INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS}, author={Mukherjee, Sayak and Babaei, Saman and Chakrabortty, Aranya and Fardanesh, Bruce}, year={2020}, month={Feb} }