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Observer-based sliding mode frequency control for micro-grid with photovoltaic energy integration

机译:光伏能量集成的微电网基于观测器的滑模频率控制

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In recent years, renewable energy has been largely integrated into power grids as well as micro-grids. The intermittent power injection will affect the stability of the systems, especially for the small-scale ones, such as the micro-grids. Among all the stability issues, load frequency control (LFC) of smart grid with renewable energy integration has become critical in the community. This paper studies the LFC for the island micro-grid with photovoltaic (PV) renewable energy integration, in which electric vehicles (EVs) are treated as distributed energy storage. With the changing load demand and PV power, the frequency of the micro-grid would deviate from the prescribed level. A observer-based sliding mode control is proposed to address this problem, where neural networks are used to implement the online observation of the PV power generation. Simulation analysis is carried out on the benchmark micro-grid, and the proposed frequency control method is effective for stabilizing the frequency to the command value over the PI and fuzzy control.
机译:近年来,可再生能源已基本集成到电网和微电网中。间歇性的功率注入将影响系统的稳定性,特别是对于诸如微型电网之类的小规模系统而言。在所有稳定性问题中,具有可再生能源集成的智能电网的负载频率控制(LFC)在社区中已变得至关重要。本文研究了具有光伏(PV)可再生能源集成的岛型微电网的LFC,其中将电动汽车(EV)视为分布式储能。随着负载需求和PV功率的变化,微电网的频率将偏离规定的水平。提出了一种基于观察者的滑模控制来解决该问题,其中使用神经网络来实现光伏发电的在线观察。在基准微电网上进行了仿真分析,所提出的频率控制方法对于通过PI和模糊控制将频率稳定在指令值是有效的。

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