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2 results on '"Chen-zhong Wang"'

1. Stepwise Inertial Intelligent Control of Wind Power for Frequency Regulation Based on Stacked Denoising Autoencoder and Deep Neural Network

Author

WANG Yalun, ZHOU Tao, CHEN Zhong, WANG Yi, QUAN Hao

Subjects
stepwise inertial control (sic), secondary frequency drop (sfd), sparrow search algorithm (ssa), stacked denoising autoencoder (sdae), deep neural network (dnn), Engineering (General). Civil engineering (General), TA1-2040, Chemical engineering, TP155-156, Naval architecture. Shipbuilding. Marine engineering, VM1-989
Abstract

Stepwise inertial control (SIC) provides a step-increase of power after load fluctuation, which can effectively prevent system frequency decline and ensure the safety of grid frequency. However, in the power recovery stage, secondary frequency drop (SFD) is easy to occur. Therefore, it is necessary to optimize SIC to obtain a better frequency regulation effect. The traditional method has the disadvantages of high calculation dimension and long consuming time, which is difficult to meet the requirements of providing the optimal control effect in different scenarios. In order to realize the optimal stepwise inertial fast control of wind power frequency regulation in load disturbance events, this paper introduces the deep learning algorithm and proposes a stepwise inertial intelligent control of wind power for frequency regulation based on stacked denoising autoencoder(SDAE) and deep neural network(DNN). First, sparrow search algorithm (SSA) is used to obtain the optimal parameters, and SDAE is used to extract the data features efficiently. Then, DNN is used to learn the data features, and the accelerated adaptive moment estimation is introduced to optimize the network parameters to improve the global optimal parameters of the network. Finally, the stepwise inertial online control of wind power frequency regulation after disturbance event is realized according to SDAE-DNN. The simulation analysis is conducted for a single wind turbine and a wind farm in the IEEE 30-bus test system. Compared with the results obtained by the traditional method, shallow BP neural network and original DNN network, it is found that the proposed network structure has a better prediction accuracy and generalization ability, and the proposed method can achieve a great effect of stepwise inertia frequency regulation.

Published
2023
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2. Biomass Production and Carbon Stocks in Poplar-Crop Agroforestry Chronosequence in Subtropical Central China

Author

Chen, Zhong Wang, Wende Yan, Yuanying Peng, Meng Wan, Taimoor Hassan Farooq, Wei Fan, Junjie Lei, Chenglin Yuan, Wancai Wang, Yaqin Qi, and Xiaoyong

Subjects
productivity, carbon storage, sustainability, land use, multi-aged stands
Abstract

Agroforest systems have been widely recognized as an integrated approach to sustainable land use for addressing the climate change problem because of their greater potential to sequester atmospheric CO2 with multiple economic and ecological benefits. However, the nature and extent of the effects of an age-sequence of agroforestry systems on carbon (C) storage remain largely unknown. To reveal the influence of different aged poplar-crop systems on C stocks, we investigated the variation in biomass and C storage under four aged poplar-crop agroforest systems (3-, 9-, 13-, and 17-year-old) in the Henan province of China. The results showed that stand biomass increased with forest age, ranging from 26.9 to 121.6 t/ha in the corresponding four aged poplar-crop systems. The poplar tree biomass accounted for >80% of the total stand biomass in these poplar-crop agroforestry systems, except in the 3-year-old agroforestry system. The average stand productivity peaked in a 9-year-old poplar-crop system (11.8 t/ha/yr), the next was in 13- and 17-year-old agroforestry systems, and the minimum was found in 3-year-old poplar-crop stands (4.8 t/ha/yr). The total C stocks increased, with aging poplar-crop systems ranging from 99.7 to 189.2 t/ha in the studied agroforestry systems. The proportion of C stocks accounted for about 6, 25, and 69% of the total C stocks in the crop, poplar tree, and soil components in all studied agroforestry ecosystems, respectively. Our results suggested that the poplar-crop system, especially in productive and mature stages, is quite an effective agroforestry model to increase the study site’s biomass production and C stocks. This study highlighted the importance of agroforestry systems in C storage. It recommended the poplar-crop agroforest ecosystems as a viable option for sustainable production and C mitigation in the central region of China.

Published
2023
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