粒子群优化
计算机科学
升压变换器
解算器
整流器(神经网络)
电子工程
风力发电
启发式
工程类
电气工程
电压
算法
人工神经网络
随机神经网络
机器学习
循环神经网络
人工智能
程序设计语言
作者
Giulia Di Nezio,Marco Di Benedetto,Alessandro Lidozzi,Luca Solero
标识
DOI:10.1109/iccep57914.2023.10247400
摘要
In the field of energy generation from renewables, such as wind power, it is essential to avoid service interruption, as well as to assure scheduled maintenance, in order to limit the operating costs of the power plant. For this reason, it is important to monitor critical system components, such as the power electronic converter. This paper proposes an online real-time monitoring method based on the Digital Twin (DT) concept of an AC-DC 3-phase converter. Based on the sensor measurement data, the DT is realized as a real-time digital model (RTDM) that runs in parallel to the physical 3-phase boost rectifier for its entire lifecycle. The Typhoon solver is used to implement the RTDM of the 3-phase boost rectifier to reach a solving time within 1µs. To identify the current value of the degradation parameters of the physical system, the comparison between the data analysis and the measurement data is performed. This scope is reached by applying the heuristic optimization algorithm like Particle Swarm Optimization (PSO) method. The proposed monitoring method is validated through the RTDM implemented on FPGA and runs on a suitable control board. Experimental tests on the converter prototype are illustrated.
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