With the availability of high-resolution data due to sensor technology advancement, it is now easier for researchers and scientists to detect or view the spectral variability of different crops. For this study, Leaf c...
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With the availability of high-resolution data due to sensor technology advancement, it is now easier for researchers and scientists to detect or view the spectral variability of different crops. For this study, Leaf chlorophyll content (LCC) and Leaf area index (LAI) of the crops Maize (Zea mays), Mustard (Brassica), and pink Lentils (Lens esculenta) under different irrigation and fertilizer treatments have been analyzed. In total, rigorous assessment of 25-hyperspectral vegetation indices (VIs) at both leaf and canopy level for chlorophyll content, whereas 7-hyperspectral VIs for LAI at canopy level were computed to investigate the robustness of these VIs for LCC and LAI assessment. Variable importance in projection (VIP) using Partial Least Square regression (PLSR) and coefficient of determination (R2) were computed for all the VIs to extract the most sensitive information for the retrieval of LCC and LAI. As a result, the VIs using the red-edge reflectance bands at 705 and 750 nm were found highly responsive to LAI compared to other wavebands. In contrast, the VIs indices made of green (550 nm), red (670, 690, and 700 nm), and red-edge (705, 750 nm) bands were found highly sensitive to the temporal LCC values of lentils and maize crop beds. In addition, the temporal LCC values of Mustard crop beds' were found sensitive to the VIs made of green (550 nm), red (670, 690, and 700 nm), and NIR (800 nm) wavebands. The three VIs having high VIP and R2 values were selected as optimum sets of input to build support vector regression models using radial (SVR-Rad), linear (SVR-Li), polynomial (SVR-Poly), Random Forrest Regression (RFR), Partial least square regression (PLSR), and Hybrid neural fuzzy inference system (HyFIS). The analysis showed that the SVR-Rad model outperformed the SVR-Li, SVR-Poly, RFR, PLSR, and HyFIS models in terms of robustness for biophysical and biochemical parameters retrieval using hyperspectral data.
A frequency control method for distributed energy storage cluster control is proposed to address the issue of poor frequency regulation performance in the power system due to the widespread integration of new energy s...
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ISBN:
(纸本)9798350378467;9798350367676
A frequency control method for distributed energy storage cluster control is proposed to address the issue of poor frequency regulation performance in the power system due to the widespread integration of new energy sources. Firstly, build a system frequency control architecture for distributed energy storage clusters, and unify the regulation of energy storage clusters through aggregation control centers. Then, design an enhancedlearning algorithm controller and use the BP neural network optimized by the improved particle swarm optimization algorithm to find the optimal energy storage power increment for frequency control. Finally, use the control output of the controller to achieve system frequency control. Based on the IEEE 39 node system, experimental analysis was conducted on the proposed method, and the results showed that the system frequency did not experience overshoot, and the energy storage output power did not exceed the limit of 75MW, significantly improving the frequency control capability of the new power system.
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