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Transient instability is an issue for grid-following converters operating under grid faults when complying with low-voltage ride-through requirements. This has initiated much research with the aim to understand, model, and prevent loss of synchronization for synchronous-reference frame phase-locked loop (SRF-PLL)-synchronized systems. However, as the majority of grid faults are asymmetrical, a more complex synchronization unit is needed for the extraction of voltage sequences and phase tracking. This paper proposes a method for enhanced transient stability during severe grid faults for more complex synchronization structures designed to deal with asymmetrical fault conditions. This is done by freezing the frequency of a stationary-reference frame frequency-locked loop. The global asymptotic stability of the method is mathematically proven, and its performance is experimentally verified. Based on the mathematical equivalence between frequency-locked loops and phase-locked loops, it is shown that the presented method can be generalized to both stationary-reference and synchronous-reference frame structures and can, therefore, be a suitable solution in a wide range of applications.

Smart grid applications belong to a diverse set of technologies, which combine behaviors and actions of all grid actors. Building such application is a challenging task requiring modeling, integrating, and validating different grid aspects efficiently. The design flow should adapt to the grid's requirements ranging from basic appliance functions to complex load scheduling algorithms and their integration's consequences. This paper tackles such challenges by proposing a framework to describe smart grid elements formally along with defining their interactions. The framework provides a smooth way for upgrading grid components independently and evaluate their performance. A case study utilizing an infrastructure of electric vehicles is demonstrated to validate the framework and prove its applicability in modeling smart grid applications. The case study enables a demand response service that provides a solution to the coordinated charging scheduling problem of electric vehicles.

The purpose of this paper is to develop a method to combine linear models with MLP networks, i.e. to find a method to make a nonlinear and multivariable model that performs at least as good as a linear model, when the training data lacks information. First, the MLP network for predicting the output from a dynamic system is described. Then two methods are proposed to combine linear and nonlinear modelling. The first method is the MLP network with linear path through, and the second method is a linear model with nonlinear error correction. Finally the two methods are tested. A thermal mixing process is used as a test system. This system is a multivariable and nonlinear process. The test is partly based on a simulation of the process and partly on data from a physical process. The results are given and discussed. >