Online dictionary learning is particularly useful for processing large-scale and dynamic data in computer vision. It, however, faces the major difficulty to incorporate robust functions, rather than the square data fitting term, to handle outliers in training data. In this paper, we propose a new online framework enabling the use of ℓ 1 sparse data fitting term in robust dictionary learning, notably enhancing the usability and practicality of this important technique. Extensive experiments have been carried out to validate our new framework.
Energy storage (ES) has become increasingly important in modern power system, whereas no single type of ES element can satisfy all diverse demands simultaneously. This study proposes a hybrid energy storage system (HESS) based on superconducting magnetic energy storage (SMES) and battery because of their complementary characteristics for the grid integration of wind power generations (WPG). This study investigates the mathematical model and the topology of the proposed HESS, which is equipped with a grid-side DC/AC converter, a battery buck/boost converter and a SMES DC chopper. The advanced control strategies comprised of device level and system level are designed. The control strategy for the converters which can be considered as device level is briefly discussed. The significant contribution of this study is proposing a novel system-level control strategy for reasonable and effective power allocation between SMES and battery. According to the control objectives, a fuzzy logic controller optimised with genetic algorithm is adopted. The detailed controller designs are described, meanwhile system stability and HESS operation performance are evaluated. MATLAB simulations are presented to demonstrate the effectiveness of the proposed strategies.
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