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Optimized PI controller for 7-level inverter to aid grid interactive RES controller
Journal of Central South University ( IF 4.4 ) Pub Date : 2021-01-28 , DOI: 10.1007/s11771-021-4593-1
K. S. Gayathri Devi , P. Sujatha Therese

With the huge rise of energy demand, the power system in the current era is moving to a new standard with increased access to renewable energy sources (RESs) integrated with distribution generation (DG) network. The RESs necessitate interfaces for controlling the power generation. The multilevel inverter (MLI) can be exploited for RESs in two diverse modes, namely, the power generation mode (stand-alone mode), and compensator mode (statcom). Few works have been carried out in optimization of controller gains with the load variations of the single type such as reactive load variation in different cases. Nevertheless, this load type may be unbalanced hence, to overcome such issues. So, a sophisticated optimization algorithm is important. This paper aims to introduce a control design via an optimization assisted PI controller for a 7-level inverter. In the present technique, the gains of the PI controller are adjusted dynamically by the adopted hybrid scheme, grey optimizer with dragon levy update (GD-LU), based on the operating conditions of the system. Here, the gains are adjusted such that the error between the reference signal and fault signal should be minimal. Thus, better dynamic performance could be attained by the present optimized PI controller. The proposed algorithm is the combined version of grey wolf optimization (GWO) and dragonfly algorithm (DA). Finally, the performance of the proposed work is compared and validated over other state-of-the-art models concerning error measures.



中文翻译:

针对7级逆变器的优化PI控制器,以辅助电网交互式RES控制器

随着能源需求的巨大增长,当前时代的电力系统正朝着新的标准迈进,增加了对与配电网(DG)网络集成的可再生能源(RES)的访问。RES需要用于控制发电的接口。多电平逆变器(MLI)可以用于两种不同模式的RES,即发电模式(独立模式)和补偿器模式(statcom)。对于单一类型的负载变化(例如在不同情况下的无功负载变化),优化控制器增益的工作很少进行。然而,这种负载类型可能因此不平衡,以克服这些问题。因此,复杂的优化算法很重要。本文旨在通过7级逆变器的优化辅助PI控制器介绍控制设计。在本技术中,PI控制器的增益由所采用的混合方案,带有龙征更新的灰度优化器(GD-LU)根据系统的运行条件进行动态调整。在此,调整增益,以使参考信号和故障信号之间的误差最小。因此,通过当前优化的PI控制器可以获得更好的动态性能。提出的算法是灰狼优化(GWO)和蜻蜓算法(DA)的组合版本。最后,与其他有关错误度量的最新模型进行了比较,并比较了所提出工作的性能。根据系统的运行条件。在此,调整增益,以使参考信号和故障信号之间的误差最小。因此,通过当前优化的PI控制器可以获得更好的动态性能。提出的算法是灰狼优化(GWO)和蜻蜓算法(DA)的组合版本。最后,与其他有关错误度量的最新模型进行了比较,并比较了所提出工作的性能。根据系统的运行条件。在此,调整增益,以使参考信号和故障信号之间的误差最小。因此,通过当前优化的PI控制器可以获得更好的动态性能。提出的算法是灰狼优化(GWO)和蜻蜓算法(DA)的组合版本。最后,与其他有关错误度量的最新模型进行了比较,并比较了所提出工作的性能。

更新日期:2021-01-29
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