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Distributed photovoltaic power generation can efficiently utilize idle resources and reduce carbon emissions. In order to reduce the impact of grid-connected distributed photovoltaic power fluctuations on grid operation, this paper simultaneously exploits the temporal dependence of power series and the spatial correlation of meteorological data to propose a combined prediction model with temporal characteristics and spatial relationships fused for distributed photovoltaic power plants with spatiotemporal information. First, in the study of time-dependent prediction, we propose a long and short-term memory neural network ensemble prediction model based on genetic algorithm-natural gradient boosting, which efficiently fits multiple sets of temporal characteristics of distributed photovoltaic. In the study of spatial correlation prediction, the meteorological data affecting photovoltaic power generation are selected by κ correlation coefficients, the target power plant and spatial reference power plant meteorological data are reconstructed into a two-dimensional matrix, and a two-dimensional convolutional neural network spatial feature extraction power prediction model is designed. Finally, the advantages of the two prediction models of temporal information and spatial features are fused by multiple error evaluation criteria improved information entropy, and a distributed photovoltaic power plant is constructed and implements highly accurate spatiotemporal information combination prediction model. The effect of the forecasting model in this study is validated using the photovoltaic cluster dataset in Hebei Province, China. Compared with other models, the results of this study show that the five prediction performance evaluation metrics of the proposed combined spatiotemporal information model are better. [ABSTRACT FROM AUTHOR]

To cope with the problems of cumbersome modeling and strongly disturbances in the control of floating offshore wind turbines (FOWTs), an improved model-free adaptive control (MFAC) strategy considering both wind speed disturbance and floating platform motion response to wave disturbance is proposed. In this strategy, a detailed mathematical model of the FOWT is avoided and the feedback controller is designed based on improved MFAC using only the input–output data to dynamically linearize the FOWT. Strongly disturbances caused by random wind and floating platform motion response to wave are compensated by a feedforward controller. The proposed method and the comparison method including the baseline controller are simulated under different wind scenarios using FAST. The results show that the proposed controller regulates the generator speed more smoothly and closer to the rated value, suppresses the motion of the floating platform more effectively, and can significantly improve the performance of FOWTs. [ABSTRACT FROM AUTHOR]

This paper describes a leaching-purifying-precipitation process to recover magnesium from serpentine acid-leaching solution and to synthesize high purity Mg(OH)2 and 4MgCO3·Mg(OH)2·4H2O. Fe, Al, and Cr in the leaching solution were removed using the oxidation precipitation method with active MgO as a precipitant and H2O2 as an oxidant. Ni, Co and Mn were removed by Na2S precipitation to obtain a pure MgSO4 solution. Mg2+ ions were first precipitated with NH3·H2O to synthesize Mg(OH)2, followed by NH4HCO3 precipitation to obtain 4MgCO3·Mg(OH)2·4H2O. A small part of MgSO4 coprecipitates with Mg(OH)2 to form MgSO4·5Mg(OH)2·3H2O. The Mg(OH)2 was aged with a diluted NaOH solution to remove the sulfur; the two-stage precipitation percentage of Mg is 96.3%. Mg(OH)2 has a purity of 98.48% with a sulfur content of 0.28%. This process provides a promising method for the high-efficiency recovery of Mg and the large-scale production of the high purity of Mg(OH)2 from the serpentine. [ABSTRACT FROM AUTHOR]