The Low Voltage Ride Through (LVRT) technology with low-speed direct-drive Permanent Magnet Synchronous Generator (PMSG) is studied in this paper. Based on the system structure and its controller options, a class of LVRT control strategies are proposed in terms of energy flow, which can make sufficient use of power characteristics of wind turbine and the large moment inertia of the system to achieve LVRT and are suitable for various running conditions. Simulation results verify that the proposed control strategy meet the LVRT requirements thoroughly and demonstrate superior dynamic performance.
In this paper, we investigate a synchronization-based, data-driven clustering approach for the analysis of functional magnetic resonance imaging (fMRI) data, and specifically for detecting functional activation from fMRI data. We first define a new measure of similarity between all pairs of data points (i.e., time series of voxels) integrating both complete phase synchronization and amplitude correlation. These pairwise similarities are taken as the coupling between a set of Kuramoto oscillators, which in turn evolve according to a nearest-neighbor rule. As the network evolves, similar data points naturally synchronize with each other, and distinct clusters will emerge. The clustering behavior of the interaction network of the coupled oscillators, therefore, mirrors the clustering property of the original multiple time series. The clustered regions whose cross-correlation coefficients are much greater than other regions are considered as the functionally activated brain regions. The analysis of fMRI data in auditory and visual areas shows that the recognized brain functional activations are in complete correspondence with those from the general linear model of statistical parametric mapping, but with a significantly lower time complexity. We further compare our results with those from traditional K-means approach, and find that our new clustering approach can distinguish between different response patterns more accurately and efficiently than the K-means approach, and therefore more suitable in detecting functional activation from event-related experimental fMRI data.
We sought to analyze the dynamic properties of brain electrical activity from healthy volunteers and epilepsy patients using recurrence networks. Phase-space trajectories of synchronous electroencephalogram signals were obtained through embedding dimension in phase-space reconstruction based on the distance set of space points. The recurrence matrix calculated from phase-space trajectories was identified with the adjacency matrix of a complex network. Then, we applied measures to characterize the complex network to this recurrence network. A detailed analysis revealed the following: (1) The recurrence networks of normal brains exhibited a sparser connectivity and smaller clustering coefficient compared with that of epileptic brains; (2) the small-world property existed in both normal and epileptic brains consistent with the previous empirical studies of structural and functional brain networks; and (3) the assortative property of the recurrence network was found by computing the assortative coefficients; their values increased from normal to epileptic brain which accurately suggested the difference of the states. These universal and non-universal characteristics of recurrence networks might help clearly understand the underlying neurodynamics of the brain and provide an efficient tool for clinical diagnosis.
Shunt Active Power Filters (APFs) are powerful in harmonic elimination and reactive power compensation in distribution network. The APF prototype discussed in this paper is based on conventional three-leg converter with an improved LCL filter in the ac side, which consists of a LC resonant branch and is tuned at switching frequency. It is helpful to suppress switching ripple injected to the grid and reduce the power loss in the damping resistor. What's more, a compound current control strategy with an outer loop of repetitive control and an inner one of Proportional-Integral (PI) control is adopted. The inner loop is used to guarantee the dynamic response, and the outer one is designed to redeem the inherent phase lag of inner loop.Consequently, excellent harmonic current tracking performance both in steady-state and dynamical-state are achieved.Experimental results of the laboratory prototype prove its feasibility and efficiency.
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