基于深度学习的并网逆变器减谐波增强功率

Subramanya Sarma S, K. Sarada, P. Jithendar, Telugu Maddileti, G. Nanda Kishor Kumar
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摘要

随着可再生能源系统的使用越来越多,并网逆变器的数量也越来越多,由于注入的电流谐波,这会对电网电力的优越性和稳定性产生不利影响。本文研究了比例积分(PI)和比例谐振(PR)控制器在并网逆变器中降低谐波的效果。研究了谐波补偿器(HC)对控制策略的影响。研究结果表明,在同步框架中实施PI和PR控制器可以有效地降低并网逆变器的注入电流谐波。谐波补偿器的使用可以通过减少失真和提高电网的稳定性来进一步提高控制器的性能。调节策略的效率取决于电网中谐波的类型和水平,以及控制器和补偿器的设计和调整。“PR+HC控制器输出电流质量优越”的说法更具体,表明这种控制方法在减少谐波和启发生产电流值方面可能比其他控制方法更有效。来自不同制造商的三种可行逆变器对IEEE 1547标准的比较也值得注意,因为它可以深入了解不同类型逆变器与标准的兼容性和性能。利用RCNN网络的深度学习来分析谐波并提供有关功率的信息是机器学习在电力系统研究中的一个有趣应用。这种方法有可能提高并网逆变器谐波分析和功率监测的准确性和能力。总体而言,该研究强调了有效控制策略对于管理并网逆变器谐波的重要性,特别是在可再生能源系统使用日益增加的背景下。这项研究的发现可以为开发更高效、更可靠的并网逆变器提供信息,这对于将可再生能源系统并入电网至关重要。
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A Deep Learning Based Enhancing the Power by Reducing the Harmonics in Grid Connected Inverters
The increasing use of renewable energy systems has led to a rise in the number of grid-connected inverters, which can have a detrimental effect on the superiority and constancy of grid electricity due to the injected current harmonics. In this study, the proportional integral (PI) and proportional resonant (PR) controllers have been investigated for their effectiveness in reducing harmonics in grid-connected inverters. The study also investigates the impact of harmonics compensators (HC) on the control strategies. The results of the study suggest that the implementation of PI and PR controllers in the synchronous frame can effectively reduce the injected current harmonics in grid-connected inverters. The use of harmonics compensators can further enhance the performance of the controllers by reducing the distortion and improving the stability of the grid. The efficiency of the regulator strategies be contingent on the type and level of harmonics in the grid, as well as the design and tuning of the controllers and compensators. The statement that the “PR+HC controller has a superior quality output current” is more specific and suggests that this control method may be more effective than the others in reducing harmonics and enlightening the value of the productivity current. The comparison of the IEEE 1547 standard by three viable inverters from diverse constructors is also noteworthy, as it can provide insights into the compatibility and performance of different types of inverters with the standard. The use of deep learning with the RCNN network for analyzing harmonics and providing information about power is an interesting application of machine learning in power systems research. This approach may have the probable to development the accuracy and competence of harmonics analysis as well as power monitoring in grid-connected inverters. Overall, the study highlights the importance of effective control strategies for managing harmonics in grid-connected inverters, particularly in the context of the increasing usage of renewable energy systems. The findings of the study can inform the development of more efficient and reliable grid-connected inverters, which are essential for the incorporation of renewable energy systems into the power grid.
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