Abstract This paper improves the performance of contribution of photovoltaic (PV) panels to the stabilization of a stand-alone hybrid microgrid during frequency excursions. Recent growth in converter-interfaced topologies and using renewable energy resources has led to decrease in inertia of isolated micro-grids (MGs) and presented some challenges to the frequency control and strength of hybrid ones. One of the effective measures to resolve mentioned problems is to deploy some potential energy resources like PV by considering a small proportion of solar panels’ generation as headroom. In addition, other distributed energy resources (DERs), which can be regarded as ancillary resources, can play an active role in the resiliency of MG’s performance in the low-inertia power systems. To this effective contribution considering some uncertainties of the microgrid parameters like the time constant of the micro-turbine, the time constant of the governor, the speed droop regulation constant as well as the load damping coefficient, an adaptive fuzzy mechanism can be proposed to determine the level of active power injection. Recurrent Adaptive Neuro Fuzzy Inference System (ANFIS) technique trains this non-linear adaptable droop to deal with uncertainties in a reasonable way. In order to ascertain parameters of membership functions in an intelligent way, this droop takes advantage of Artificial Bee Colony (ABC) algorithm based on a multi-objective. The Simulation results verify the robustness and reliability of this flexible fuzzy droop during different operating conditions as well as intermittent nature of renewable energy resources such as wind turbine generators in hybrid micro-grid.

中文翻译：

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参数不确定时光伏对混合微电网频率偏移贡献的自适应模糊下垂学习

摘要 本文改进了光伏 (PV) 面板在频率偏移期间对独立混合微电网稳定性的贡献性能。最近转换器接口拓扑的增长和可再生能源的使用导致孤立微电网 (MG) 惯性的降低，并对混合微电网的频率控制和强度提出了一些挑战。解决上述问题的有效措施之一是考虑将太阳能电池板发电量的一小部分作为净空，部署一些潜在的能源，如光伏。此外，可被视为辅助资源的其他分布式能源（DER）可以在低惯量电力系统中 MG 性能的弹性中发挥积极作用。考虑到微电网参数的一些不确定性，如微涡轮机的时间常数、调速器的时间常数、速度下垂调节常数以及负载阻尼系数，对于这种有效贡献，可以提出一种自适应模糊机制来确定有功功率注入水平。循环自适应神经模糊推理系统 (ANFIS) 技术训练这种非线性自适应下垂以合理的方式处理不确定性。为了以智能方式确定隶属函数的参数，该下垂利用基于多目标的人工蜂群 (ABC) 算法。