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Intelligent islanding detection method for photovoltaic power system based on Adaboost algorithm

机译：基于Adaboost算法的光伏电力系统智能岛检测方法

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摘要

The universal islanding detection methods (IDMs) for photovoltaic (PV) power systems require manually thresholds setting. That will lead to a certain non-detection zone (NDZ). Moreover, disturbance signals injected by active detection methods may adversely affect power quality. Aiming at the above problems, this study proposes a passive intelligent IDM for parallel multi-PV system based on improved Adaptive Boosting (Adaboost) algorithm. Using Adaboost algorithm to generate classification models for islanding detection can theoretically avoid the NDZ of passive methods. The proposed method takes advantage of the electrical connection between characteristic parameters to adjust the classification model and improves the detection ability by redistributing the weight of each sub-model. Simulation results show that when adopted to a multi-PV system, the proposed method can effectively distinguish islanding operation in the NDZs of conventional passive IDMs. The method can also achieve accurate detection in the case of short-term power quality interferences, line faults and disturbance signal interference injected by active methods.
机译：用于光伏（PV）电力系统的通用孤岛检测方法（IDMS）需要手动阈值设置。这将导致某个非检测区（NDZ）。此外，通过主动检测方法注入的扰动信号可能会对电力质量产生不利影响。针对上述问题，本研究提出了一种基于改进的自适应升压（Adaboost）算法的并联多光伏系统的被动智能IDM。使用AdaBoost算法生成岛屿检测的分类模型，理论上可以避免被动方法的NDZ。所提出的方法利用特征参数之间的电连接来调整分类模型，通过重新分配每个子模型的权重来改善检测能力。仿真结果表明，当采用多光伏系统时，该方法可以有效地区分常规无源IDMS的NDZ中的岛屿操作。该方法还可以在短期功率质量干扰，线路故障和干扰信号干扰的情况下实现精确的检测，通过主动方法注入。

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