5 Search Results

A high-dimensional neural network interatomic potential was developed and used in molecular dynamics simulations of condensed phase Ni and Ni systems with liquid–solid phase coexistence. The reference data set was generated by sampling the potential energy surface over a broad temperature-pressure domain using ab initio MD simulations to train a unified potential. Excellent agreement was achieved between bulk face-centered cubic nickel thermal expansion simulations and relevant experimental data. The same potential also yields accurate structures and diffusivities in the liquid state. The phase transition between liquid and solid phases was simulated using the two-phase interface method. The predicted melting point temperature is within a few kelvins of the literature value. Here, the general methodology could be applied to describe crystals with much more complex phase behaviors.

This article presents a novel decentralized filtering adaptive neural network control framework for uncertain switched interconnected nonlinear systems. Each subsystem has its own decentralized controller based on the established decentralized state predictor. For each subsystem, the nonlinear uncertainties are approximated by a Gaussian radial basis function (GRBF) neural network incorporated with a piecewise constant adaptive law, where the adaptive law will update adaptive parameters from the error dynamics between the host system and the decentralized state predictor by discarding the unknowns, whereas a decentralized filtering control law is derived to cancel both local and mismatched uncertainties from other subsystems, as well as achieve the local objective tracking of the host system. The achievement of global objective depends on the achievement of local objective for each subsystem. The matched uncertainties are canceled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is required to eliminate the effect of the mismatched uncertainties on the output. By exploiting the average dwell time principle, the error bounds between the real system and the virtual reference system, which defines the best performance that can be achieved by the closed-loop system, are derived. A numerical example is given to illustrate the effectiveness of the decentralized filtering adaptive neural network control architecture by comparing against the model reference adaptive control (MRAC).

This study is motivated by the recent advancements in developing non-contact line sensor technologies that come at a low cost, but have limited measurement capabilities. While they are intended to measure current, they cannot measure voltage and power. This poses a challenge to certain distribution system applications, such as topology identification (TI), because they commonly use voltage and power measurements. To address this open problem, a new TI algorithm is proposed based on measurements from a few line current sensors, together with available pseudo-measurements for nodal power injections. A TI problem formulation is first developed in the form of a mixed integer nonlinear program (MINLP). Several reformulation steps are then adopted to tackle the nonlinearities to express the TI problem in the form of a mixed integer linear program (MILP). The proposed method is able to identify all possible topologies, including radial, loop, and island configurations, which extends the application of TI to identify switch malfunctions and to detect outages. In addition, recommendations are made with respect to the number and location of the line current sensors to ensure performance accuracy of the TI method. Here, a novel multi-period TI algorithm is also proposed to use multiple measurement snapshots to improve the TI accuracy and robustness against errors in pseudo-measurements. The effectiveness of the proposed TI algorithms is examined on the IEEE 33-bus test case as well as a test case based on a real-world feeder in Riverside, CA.

This paper proposes a sequential convex optimization method to solve broader classes of optimal power flow (OPF) problems over radial networks. The non-convex branch power flow equation is decomposed as a second-order cone inequality and a non-convex constraint involving the difference of two convex functions. Provided with an initial solution offered by an inexact second-order cone programming relaxation model, this approach solves a sequence of convexified penalization problems, where concave terms are approximated by linear functions and updated in each iteration. It could recover a feasible power flow solution, which usually appears to be very close, if not equal, to the global optimal one. Two variations of the OPF problem, in which non-cost related objectives are optimized subject to power flow constraints and the convex relaxation is generally inexact, are elaborated in detail. One is the maximum loadability problem, which is formulated as a special OPF problem that seeks the maximal distance to the boundary of power flow insolvability. The proposed method is shown to outperform commercial nonlinear solvers in terms of robustness and efficiency. The other is the bi-objective OPF problem. A non-parametric scalarization model is suggested, and is further reformulated as an extended OPF problem by convexifying the objective function. It provides a single trade-off solution without any subjective preference. The proposed computation framework also helps retrieve the Pareto front of the bi-objective OPF via the e-constraint method or the normal boundary intersection method. This paper also discusses extensions for OPF problems over meshed networks based on the semidefinite programming relaxation method.

This report examines six case studies of photovoltaic systems integrated into secondary network systems in four major U.S. Solar America cities.