Browsing by Author "Kekatos, Vassilis"

Now showing 1 - 6 of 6
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    Efficient Topology Design Algorithms for Power Grid Stability
    Bhela, Siddharth; Nagarajan, Harsha; Deka, Deepjyoti; Kekatos, Vassilis (IEEE, 2022-01-01)
    The dynamic response of power grids to small disturbances influences their overall stability. This letter examines the effect of network topology on the linearized time-invariant dynamics of electric power systems. The proposed framework utilizes ${\mathcal{ H}}_{2}$ -norm based stability metrics to study the optimal placement of lines on existing networks as well as the topology design of new networks. The design task is first posed as an NP-hard mixed-integer nonlinear program (MINLP) that is exactly reformulated as a mixed-integer linear program (MILP) using McCormick linearization. To improve computation time, graph-theoretic properties are exploited to derive valid inequalities (cuts) and tighten bounds on the continuous optimization variables. Moreover, a cutting plane generation procedure is put forth that is able to interject the MILP solver and augment additional constraints to the problem on-the-fly. The efficacy of our approach in designing optimal grid topologies is demonstrated through numerical tests on the IEEE 39-bus network.
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    Inferring Power System Dynamics from Synchrophasor Data using Gaussian Processes
    Jalali, Mana; Kekatos, Vassilis; Bhela, Siddharth; Zhu, Hao; Centeno, Virgilio A. (2022-01-01)
    Synchrophasor data provide unprecedented opportunities for inferring power system dynamics, such as estimating voltage angles, frequencies, and accelerations along with power injection at all buses. Aligned to this goal, this work puts forth a novel framework for learning dynamics after small-signal disturbances by leveraging Gaussian processes (GPs). We extend results on learning of a linear time-invariant system using GPs to the multi-input multi-output setup. This is accomplished by decomposing power system swing dynamics into a set of single-input single-output linear systems with narrow frequency pass bands. The proposed learning technique captures time derivatives in continuous time, accommodates data streams sampled at different rates, and can cope with missing data and heterogeneous levels of accuracy. While Kalman filter-based approaches require knowing all system inputs, the proposed framework handles readings of system inputs, outputs, their derivatives, and combinations thereof collected from an arbitrary subset of buses. Relying on minimal system information, it further provides uncertainty quantification in addition to point estimates of system dynamics. Numerical tests verify that this technique can infer dynamics at non-metered buses, impute and predict synchrophasors, and locate faults under linear and non-linear system models under ambient and fault disturbances.
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    Inferring Power System Frequency Oscillations using Gaussian Processes
    Jalali, Mana; Kekatos, Vassilis; Bhela, Siddharth; Zhu, Hao (IEEE, 2021-12-14)
    Synchronized data provide unprecedented opportunities for inferring voltage frequencies and rates of change of frequencies (ROCOFs) across the buses of a power system. Aligned to this goal, this work puts forth a novel framework for learning dynamics after small-signal disturbances by leveraging the tool of Gaussian processes (GPs). We extend results on inferring the input and output of a linear time-invariant system using GPs to the multi-input multi-output setup by exploiting power system swing dynamics. This physics-aware learning technique captures time derivatives in continuous time, accommodates data streams sampled potentially at different rates, and can cope with missing data and heterogeneous levels of accuracy. While Kalman filter-based approaches require knowing all system inputs, the proposed framework handles readings of system inputs, outputs, their derivatives, and combinations thereof on an arbitrary subset of buses. Relying on minimal system information, it further provides uncertainty quantification in addition to point estimates for dynamic grid signals. The required spatiotemporal covariances are obtained by exploring the statistical properties of approximate swing dynamics driven by ambient disturbances. Numerical tests verify that this technique can infer frequencies and ROCOFs at non-metered buses under (non)-ambient disturbances for a linearized dynamic model of the IEEE 300-bus benchmark.
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    Real-Time Detection of GPS Spoofing Attack with Hankel Matrix and Unwrapped Phase Angle Data
    Khan, Imtiaj (Virginia Tech, 2021-11)
    Cyber-attack on synchrophasor data has become a widely explored area. However, GPS-spoofing and FDIA attacks require different responsive actions. State-estimation based attack detection method works similar way for both types of attacks. It implies that using state-estimation based detection alone doesn’t give the control center enough information about the attack type. This scenario is specifically more critical for those attack detection methods which consider GPS-spoofing attack as another FDIA with falsified phase angle data. Since identifying correct attack type is paramount, we have attempted to develop an algorithm to distinguish these two attacks. Previous researchers exploited low-rank approximation of Hankel Matrix to differentiate between FDIA and physical events. We have demonstrated that, together with angle unwrapping algorithm, low-rank approximation of Hankel Matrix can help us separating GPS-spoofing attack with FDIA. The proposed method is verified with simulation result. It has been demonstrated that the GSA with 3 second time-shift creates a low-rank approximation error 700% higher than that of normal condition, whereas FDIA doesn’t produce any significant change in low-rank approximation error from that of normal condition. Finally, we have proposed a real-time method for successful identification of event, FDIA and GSA.
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    Resilience and Cybersecurity for Distribution Systems with Distributed Energy Resources
    Somda, Baza R. (Virginia Tech, 2023-05)
    Heightened awareness of the impact of climate change has led to rapidly increasing penetration of renewable energy resources in electric energy distribution systems. Those distributed energy resources (DERs), mostly inverter-based, can act as resiliency sources for the grid but also introduce new control and stability challenges. In this thesis, a cyber-physical system (CPS) testbed is proposed combining a real-time electro-magnetic transient power system simulation and a practical model for communication network simulation. By regularly updating the CPS testbed with real-world SCADA information, a digital twin is effectively created. The digital twin allows the testing of novel microgrid control and cybersecurity strategies. Simulations using the Virginia Tech Electric Service (VTES) as a test case demonstrate the capability of adequately controlled resources, including solar PV, energy storage, and a synchronous generator, to enhance resilience by providing energy to critical loads. The DERs comply with IEEE disturbance ride-through requirements and switching transients are maintained within acceptable limits. A comprehensive DER-based resiliency plan is developed and validated for the Virginia Tech smart grid.
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    Strategic Generation Investment in Energy Markets: A Multiparametric Programming Approach
    Taheri, Sina; Kekatos, Vassilis; Veeramachaneni, Harsha (2021)
    An investor has to carefully select the location and size of new generation units it intends to build, since adding capacity in a market affects the profit from units this investor may already own. To capture this closed-loop characteristic, strategic investment (SI) of generation can be posed as a bilevel optimization. By analytically studying a small market, we first show that its objective function can be non-convex and discontinuous. Realizing that existing mixed-integer problem formulations become impractical for larger markets and number of instances, this work put forth two SI solvers: a grid search to handle setups where the candidate investment locations are few, and a stochastic gradient descent approach for otherwise. Both solvers leverage powerful results of multiparametric programming (MPP), each in a unique way. The grid search entails finding the primal/dual solutions for a large number of optimal power flow (OPF) problems, which nonetheless can be efficiently computed several at once thanks to the properties of MPP. The same properties facilitate the rapid calculation of gradients in a mini-batch fashion, thus accelerating the implementation of a stochastic (sub)-gradient descent search. Tests on the IEEE 30- and 118-bus systems using real-world data corroborate the advantages of the novel solvers.