For realizing high-accuracy short-term wind power prediction, a hybrid model considering physical features of data is proposed in this paper, with consideration of chaotic analysis and granular computing. First, considering the chaotic features of wind power time series physically, data reconstruction in chaotic phase space is studied to provide a low-dimensional input with more information in modeling. Second, considering that meteorological scenarios of wind development are various, complicated, and uncertain, typical chaotic time series prediction models and wind scenarios are analyzed correspondingly via granular computing (GrC). Finally, through granular rule-based modeling, a hybrid model combining reconstructed wind power data and different models is constructed for short-term wind power prediction. Data from real wind farms is taken for experiments, validating the feasibility and effectiveness of the proposed wind power prediction model.
Distributed bus protection is one of the main tasks of smart substation construction to improve the secondary system because it is economical, simple, easy to layout, and easy to realize double. This paper mainly considers that distributed current differential protection is limited by high precision synchronization and high bandwidth and a distributed bus protection based on the directional impedance element comparison principle is proposed. The bus impedance protection constructed by reverse current calculation is proposed, which the impedance protection with positive sequence polarization and the impedance protection with memory polarization are the main components. Further considering the influence of system oscillation, the power frequency variable distance protection is introduced to form a distributed bus protection system, and the setting and time setting principles are given. The principle of the protection system is simple and the action is reliable. The effectiveness of the protection system is verified based on PSCAD simulation software.
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