Design and implementation of a robust ANN-PID corrector to improve high penetrations photovoltaic solar energy connected to the grid

Q3 Energy
D. Gueye, A. Ndiaye, Amadou Diao
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引用次数: 0

Abstract

The best quality of PV energy into the grid is now problematic that is why this paper focuses on the design and implementation of a robust Proportional Integral Derivative based on Artificial Neural Network (ANN-PID). This technique used to ensure the regulation of the Boost Converter (BC) output voltage and the Three Phase Inverter (3 PI) output currents of a photovoltaic solar system (PVS) connected to the grid. The mathematical model of the DC bus and the 3-PI is presented. Applications under Matlab/Simulink justify the efficiency of the neural regulator. In comparison with the conventional one, the proposed method presents the best follow-up of the DC link voltage reference and a maximum overshoot of 3.16 %. In addition, despite the long time put in transient mode, the proposed method keeps better robustness and ensures an injection of current of a total harmonic distortion (THD) of 0.96 % against 2.18 % of the classical PID regulator.
一种鲁棒ANN-PID校正器的设计与实现,以改善高穿透光伏太阳能并网
进入电网的光伏能量的最佳质量现在是个问题,这就是为什么本文专注于基于人工神经网络(ANN-PID)的鲁棒比例积分导数的设计和实现。该技术用于确保连接到电网的光伏太阳能系统(PVS)的升压转换器(BC)输出电压和三相逆变器(3PI)输出电流的调节。给出了直流母线和3-PI的数学模型。在Matlab/Simulink下的应用证明了神经调节器的有效性。与传统方法相比,所提出的方法呈现出DC链路电压参考的最佳跟随和3.16%的最大过冲。此外,尽管处于瞬态模式的时间很长,但所提出的方法保持了更好的鲁棒性,并确保了总谐波失真(THD)为0.96%的电流注入,而传统PID调节器的总谐波失真为2.18%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Energy Systems
Journal of Energy Systems Environmental Science-Management, Monitoring, Policy and Law
CiteScore
1.60
自引率
0.00%
发文量
29
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