Enhancing performance of interleaved KY converter control in single phase grid-tied PV systems: A hybrid approach

R Shobha, N Narmadhai
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Abstract

This paper proposes a hybrid COA-QNN approach for an interleaved KY converter with closed-loop control for a single-phase grid-connected photovoltaic (PV) system. The proposed strategy combines both Cheetah Optimizer algorithm (COA) and Quantum Neural Network (QNN), and it is commonly named the COA-QNN technique. The interleaved KY converter is connected in the converter side. The primary goal of the COA-QNN technique is to enhance PQ while maximizing PV electricity being transferred to the grid. The proposed COA is utilized to identify the optimal closed-loop controller enhancements for on-grid solar photovoltaic systems. The QNN is used to predict the optimal control parameter. The PV-interleaved KY converter is managed by a predictive control mechanism to carry out both tasks of PQ enhancement. By then the COA-QNN technique is implemented on the MATLAB platform and compared with existing methods. Finally, the proposed method shows better results in all methods, such as GWO, FF optimization and combined GWO-FF.

Abstract Image

提高单相并网光伏系统中交错 KY 转换器控制的性能:混合方法
本文针对单相并网光伏(PV)系统的闭环控制交错 KY 转换器提出了一种 COA-QNN 混合方法。所提出的策略结合了猎豹优化算法(COA)和量子神经网络(QNN),通常称为 COA-QNN 技术。交错式 KY 转换器连接在转换器侧。COA-QNN 技术的主要目标是提高 PQ,同时最大限度地向电网输送光伏电力。所提出的 COA 可用于确定并网太阳能光伏系统的最佳闭环控制器增强功能。QNN 用于预测最佳控制参数。光伏交错 KY 转换器由预测控制机制管理,以执行 PQ 增强的两项任务。然后,在 MATLAB 平台上实现了 COA-QNN 技术,并与现有方法进行了比较。最后,在 GWO、FF 优化和 GWO-FF 组合等所有方法中,所提出的方法都显示出更好的效果。
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