Nov 2026· IEEE transactions on power electronics· Vol 41, pp. 18839-18844· 0 citations· 15 references
Abstract
With the development of artificial intelligence, neural network technology has been applied in the power system. In this letter, a neural network direct control of static synchronous compensator (STATCOM) is proposed, which can improve the dynamic and steady-state performance. First, a simulation model of STATCOM under direct reactive power control is established, and the simulation data are collected as the training dataset for the neural network. Then, the neural network architecture and training algorithm are designed according to the input and output variables, and the neural network direct controller of STATCOM is obtained through training. Compared with direct reactive power control, the proposed neural network direct control can significantly improve the dynamic and steady-state performance of STATCOM due to the direct and fast input–output characteristics of the neural network. Finally, the effectiveness of the proposed control strategy is verified through the experiments based on TMS320F28377 chip.
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A control method based on the combination of energy control, input shaping, and neural network is proposed, in order to solve the problem of anti-swing and positioning of portal bridge cranes under non-zero initial conditions.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.
