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首页> 外文会议>International Seminar on Intelligent Technology and Its Applications >Islanding Detection in Grid-Connected Distributed Photovoltaic Generation Using Artificial Neural Network
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Islanding Detection in Grid-Connected Distributed Photovoltaic Generation Using Artificial Neural Network

机译：人工神经网络在分布式光伏发电中的孤岛检测

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Photovoltaic (PV) systems are nowadays one of the most wide-spread renewable energy systems in the network or grid with one purpose to improve the reliability of the grid. However, PV systems in the network also contribute a negative impact as well; when the main grid fails to supply the load and there is a part of the load energized by the PV systems while being isolated. This case is defined as islanding. If this condition cannot be detected, the load bus will experience voltage disturbance and power quality problem. This paper presents an islanding detection using Artificial Neural Network method (ANN). ANN learning data are generated from simulations under three main scenarios: power match, overvoltage, and undervoltage, with varying power factor (cos phi). Voltage signal at PCC node in load bus is classified to identify if system is in islanding condition or not. The simulation results shows that the built ANN is capable to detect both islanding and non-islanding mode.
机译：如今，光伏（PV）系统是网络或电网中使用最广泛的可再生能源系统之一，其目的是提高电网的可靠性。但是，网络中的光伏系统也会产生负面影响。当主电网无法提供负载，并且光伏系统中有一部分负载处于隔离状态时。这种情况被定义为孤岛。如果无法检测到此情况，则负载总线将遇到电压干扰和电能质量问题。本文提出了一种使用人工神经网络方法（ANN）的孤岛检测。在三种主要情况下，通过仿真生成ANN学习数据：功率匹配，过压和欠压，且功率因数（cos phi）有所变化。对负载总线中PCC节点处的电压信号进行分类，以识别系统是否处于孤岛状态。仿真结果表明，所构建的人工神经网络能够同时检测孤岛和非孤岛模式。

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