摘要：【中文】本发明提供了一种基于BP神经网络的微网攻击识别方法，包括采集光伏数据流；对特征数据进行预处理；将预处理后的特征数据输入至BP神经网络模型中进行实时检测分类并输出分类结果，所述分类结果包括正常类、网络攻击类，所述网络攻击类根据网络攻击行为的具体特征进行分类，包括Dos(拒绝服务)攻击、未授权访问攻击、接口端非正常探测、木马病毒攻击、电气量和气象等数据伪造和篡改、窃取电量等攻击类型；判断分类结果是否存在网络攻击类，是则根据网络攻击的类型采取相应的报警机制同时生成日志记录以及对特征数据流进行拦截；否则正常向上层转发。本发明还提供了一种并网接口装置。与现有技术相比，保证微电网的安全可靠运行。 【EN】The invention provides a microgrid attack identification method based on a BP neural network, which comprises the steps of collecting photovoltaic data flow; preprocessing the characteristic data; inputting the preprocessed feature data into a BP neural network model for real-time detection and classification and outputting classification results, wherein the classification results comprise a normal class and a network attack class, and the network attack class is classified according to specific features of network attack behaviors, and comprises Dos (denial of service) attack, unauthorized access attack, abnormal detection of an interface end, Trojan virus attack, data forgery and falsification such as electrical quantity and meteorological quantity, and attack types such as electric quantity stealing and the like; judging whether the classification result has a network attack class, if so, adopting a corresponding alarm mechanism to simultaneously generate log records and intercept the characteristic data stream according to the type of the network attack; otherwise, forwarding to the upper layer normally. The invention also provides a grid-connected interface device. Compared with the prior art, the safe and reliable operation of the micro-grid is ensured.