In this paper, two different filter structures for smart antennas based on a convex combination of independent transversal adaptive sub-filters are analyzed. The first structure combines the least-mean-squares (LMS) a...
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ISBN:
(纸本)9781509037940
In this paper, two different filter structures for smart antennas based on a convex combination of independent transversal adaptive sub-filters are analyzed. The first structure combines the least-mean-squares (LMS) and the augmented complex least-mean-squares (aclms) algorithms, whereas the second one uses the recursive least-squares (RLS) and the complex dual least-mean-squares (CDU-LMS) algorithms. The individual sub-filters are independently adapted using their own error signals, while the whole smart system is adapted by means of a convex stochastic gradient algorithm that generates an third independent error signal. The number of iterations required to reach convergence and the effects of the control parameter tau on the learning curve of the whole structure are studied. According to the simulation, these hybrid smart structures turned out to be more robust than a smart antenna that uses an unique adaptive filter. In general, both hybrid smart beamformers show to have a better filtering capacity than the standard LMS and RLS smart antenna systems. General equations for the overall output and the radiation pattern have been developed for both variations.
A new framework for the estimation of the instantaneous frequency in a three-phase power system is proposed. It is first illustrated that the complex-valued signal, obtained by the alpha beta transformation of three-p...
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ISBN:
(纸本)9781424496365
A new framework for the estimation of the instantaneous frequency in a three-phase power system is proposed. It is first illustrated that the complex-valued signal, obtained by the alpha beta transformation of three-phase power signals under unbalanced voltage sag conditions, is second order noncircular, for which standard complex adaptive estimators are suboptimal. To cater for second order noncircularity, an adaptive widely linear estimator based on the augmented complex least mean square (aclms) algorithm is proposed, and the analysis shows that this allows for optimal linear adaptive estimation for the generality of system conditions (both balanced and unbalanced). The enhanced robustness over the standard CLMS is illustrated by simulations on both synthetic and real-world voltage sags.
It is necessary to study the impact of large-scale wind turbines with virtual inertia control on power system stability considering the effects of frequency measurement. The phase-locked loop (PLL) measures the system...
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It is necessary to study the impact of large-scale wind turbines with virtual inertia control on power system stability considering the effects of frequency measurement. The phase-locked loop (PLL) measures the system frequency. The operation of the PLL is deteriorated when the voltage contains sags, unbalance, high harmonics, and sudden drop frequency. Cascaded delayed signal cancelation (DSC) PLL technique can be used for frequency measurement under nonideal voltage due to its good harmonics filtering capability. But there are some oscillations and errors in the frequency estimation by the DSC-PLL that led not to suitable frequency control in a doubly fed induction generator (DFIG). In this paper, an adaptive complex least mean square (aclms) algorithm is used for frequency estimation. The aclms algorithm is an adaptive filter, which can track frequency deviation, accurately and rapidly. In addition, this paper addresses robust frequency control in the DFIG. In a power system with wind turbines, load change, power fluctuation disturbance, and a dynamical perturbation influence the system frequency. In this condition, the conventional-inertia controller is faced with challenges. H-infinity-robust controller develops the frequency control loop. The estimated frequency using the aclms algorithm feeds the robust controller to improve the inertial response of the DFIG. This paper compares between H-infinity and optimal conventional-based inertia controller. The simulation validates that the aclms estimation with the H-infinity inertia controller simultaneously provides frequency support to a low-inertia tested power system, effectively. It improves frequency stability and resiliency under different disturbances and uncertainty.
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