We propose in this paper a fuzzy BP neural network model and DDAE-SVR deep neural network model to analyze multimodal digital teaching, establish a multimodal digital teaching quality data evaluation model based on a fuzzy BP neural network, and optimize the initial weights and thresholds of BP neural network by using adaptive variation genetic algorithm. Since the BP neural network is highly dependent on the initial weights and points, the improved genetic algorithm is used to optimize the initial weights and thresholds of the BP neural network, reduce the time for the BP neural network to find the importance and points that satisfy the training termination conditions, and improve the prediction accuracy and convergence speed of the neural network on the teaching quality evaluation results. The entropy value method, a data-based objectivity evaluation method, is introduced as the guidance mechanism of the BP neural network. The a priori guidance sample is obtained by the entropy method. Then, the adaptive variational genetic algorithm is used to optimize the BP neural network model to learn the a priori sample knowledge and establish the evaluation model, which reduces the subjectivity of the BP neural network learning sample. To better reflect and compare the effects of the two neural network evaluation models, BP and GA-BP, the sample data were continued to be input into the original GA and BSA to obtain the evaluation results and errors; then, the evaluation results of the two evaluation models, BP and GA-BP, were compared with the evaluation results of the two algorithms, GA and BSA. It was found that the GA-BP neural network evaluation model has higher accuracy and can be used for multimodal digital teaching quality evaluation, providing a more feasible solution.
Distributed static compensators (DSTATCOMs) are grid-connected power electronic equipment dedicated to compensating reactive power as well as improving voltage regulation in distribution networks. They eclipse conventional compensation approaches, such as capacitor banks, in terms of flexibility and effectiveness. Despite their identified advantages, STATCOMs with voltage droop are subject to weak grid-induced stability problems, as first revealed by this paper. Specifically, the voltage droop controller that couples the amplitude of point of common coupling (PCC) voltages to the reactive current reference creates a local control loop. Such a loop greatly deteriorates system stability in weak grids, which feature large and variable grid impedances. To address such stability problems, we propose a novel virtual resistance control scheme, which improves system stability through mitigation of local control loop gains in the low-frequency band. Experimental results obtained from a DSTATCOM prototype clearly demonstrate the correctness of stability analysis and the effectiveness of stability improvement.
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