Youping Fan

Most existing studies on the optimal bandwidth selection for plant nitrogen are based on the sensitive band center, and determine the optimal bands by manually adjusting the bandwidth, step by step. However, this method has a high level of manual involvement and is time-consuming. This paper focused on rice as the research subject, based on determining the center of the rice plant nitrogen-sensitive bands and the maximum region Ω of the fitted R² between the narrow-band vegetation indices (N-VIs) and plant nitrogen, a method was proposed to automatically select the optimal bandwidth by constructing inscribed rectangles. UAV hyperspectral images were used to carry out the spatial inversion and precision verification of the rice plant nitrogen, based on the optimal width of sensitive bands. The results revealed that the optimal bandwidths, automatically selected on the basis of N-VIs via the inscribed rectangle method, achieved good results in the remote sensing inversion of plant nitrogen at the rice jointing and flowering stages, with the coefficient of determination (R²) greater than 0.49 to satisfy the requirement of significance (p < 0.05) and the normalized root mean square error (NRMSE) and mean relative error (MRE) of less than 13%. These findings indicate that the method of crop plant nitrogen inversion band center screening and automatic search for the optimal bandwidth in this study has certain feasibility, which provides a new idea for screening the optimal bandwidth on the basis of the sensitive band center and provides technical support for the design of satellite band parameters.

The widespread use of large-scale new energy sources and flexible controllable loads has caused randomness on both sides of the power system. which challenges the traditional coordinated control method. Therefore, this paper analyzes the characteristics of bilateral randomness of new energy power system, and proposes a coordinated control strategy of new energy power system considering demand-side response. The strategy takes the maximum wind power consumption as the target in advance control, and based on the day-ahead wind power forecast and the day-ahead load forecast curve, the starting and stopping and output plans of conventional generating units are formulated. Demand side control is mainly to stabilize the active power fluctuation caused by wind power and load, and to coordinate control of demand side load according to priority. Finally, the feasibility and effectiveness of the control strategy are verified by an example simulation.

To overcome those disadvantages that BP (Back Propagation) neural network and conventional Particle Swarm Optimization (PSO) converge at the global best particle repeatedly in early stage and is easy trapped in local optima and with low diagnosis accuracy when being applied in converter transformer fault diagnosis, we come up with the improved PSO-BP neural network to improve the accuracy rate. This algorithm improves the inertia weight Equation by using the attenuation strategy based on concave function to avoid the premature convergence of PSO algorithm and Time-Varying Acceleration Coefficient (TVAC) strategy was adopted to balance the local search and global search ability. At last the simulation results prove that the proposed approach has a better ability in optimizing BP neural network in terms of network output error, global searching performance and diagnosis accuracy.