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We propose an analytically tractable Bayesian method to infer parameters in power system dynamic models from noisy measurements of bus-voltage magnitudes and frequencies as well as active- and reactive-power injections. The proposed method is computationally appealing as it bypasses the large number of system model simulations typically required in sampling-based Bayesian inference. Instead, it relies on analytical linearization of the nonlinear system differential-algebraic-equation model enabled by trajectory sensitivities. Central to the proposed method is the construction of a linearized model with the maximum probability of being (closest to) the actual nonlinear model that gave rise to the measurement data. The linear model together with Gaussian prior leads to a conjugate family where the parameter posterior, model evidence, and their gradients can be computed in closed form, markedly improving scalability for large-scale power systems. We illustrate the effectiveness and key features of the proposed method with numerical case studies for a three-bus system. Algorithmic scalability is then demonstrated via case studies involving the New England 39-bus test system. [ABSTRACT FROM AUTHOR]

This paper presents a measurement-based method to determine distributed energy resource (DER) active- and reactive-power setpoints that minimize bus voltage deviations from prescribed reference values, bus active- and reactive-power deviations from desired setpoints, as well as cost of DER outputs. Central to the proposed method is the estimation of a linear sensitivity model from synchronized voltage and power-injection data collected from distribution-level phasor measurement units installed at only a subset of buses in the distribution system. As new measurements become available, the linear sensitivity model is updated via the recursive weighted partial least-squares estimation method. The estimated sensitivity model is then embedded as an equality constraint in a convex quadratic optimization problem, which can be solved via, e.g., the alternating direction method of multipliers. Numerical simulations involving the IEEE 33-bus distribution test system illustrate key benefits of the proposed method, including (i) eliminating the need for an accurate offline system model, (ii) adapting to online network-topology and operating-point changes, and (iii) being robust against delays potentially attributed to communication, computation, and actuation. Additional numerical simulations involving larger test systems demonstrate computational scalability. [ABSTRACT FROM AUTHOR]

This paper proposes a method to directly compute controller parameter values in a synchronverter augmented with a so-called damping correction loop, and in so doing, achieve desired transient and steady-state response. The proposed approach is grounded in a reduced third-order system model that captures pertinent dynamic characteristics of the synchronverter active-power loop (APL), particularly those of the dominant mode. This reduced-order model helps to identify and explain a shortcoming in a previous parameter tuning method. Central to the proposed parameter computation method is to express APL parameters of the original system as closed-form functions of the poles of the reduced-order system. Since the reduced-order model retains dominant-mode dynamic behaviors of the original system, APL parameters can be directly computed according to specified APL dominant mode. Time-domain simulations are provided to validate the accuracy of the reduced-order model and the proposed direct-computation parameter tuning method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method to compute linear sensitivity distribution factors (DFs) in near real time without relying on a power flow model of the system. Specifically, we compute the injection shift factors (ISFs) of a particular line of interest with respect to active power injections at all buses (all other DFs can be determined from ISFs). The proposed ISF estimation method relies on the solution of an underdetermined system of linear equations that arise from high-frequency synchronized measurements obtained from phasor measurement units. We exploit a sparse representation (i.e., one in which many elements are zero) of the vector of desired ISFs via rearrangement by electrical distance and an appropriately chosen linear transformation, and cast the estimation problem into a sparse vector recovery problem. As we illustrate through case studies, the proposed approach provides accurate DF estimates with fewer sets of synchronized measurements than earlier approaches that rely on the solution of an overdetermined system of equations via the least-squares errors estimation method. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a method to compute linear sensitivity distribution factors (DFs) in near real-time. The method does not rely on the system power flow model. Instead, it uses only high-frequency synchronized data collected from phasor measurement units to estimate the injection shift factors through linear least-squares estimation, after which other DFs can be easily computed. Such a measurement-based approach is desirable since it is adaptive to changes in system operating point and topology. We further improve the adaptability of the proposed approach to such changes by using weighted and recursive least-squares estimation. Through numerical examples, we illustrate the advantages of our proposed DF estimation approach over the conventional model-based one in the context of contingency analysis and generation re-dispatch. [ABSTRACT FROM PUBLISHER]