随着我国智能变电站的推广,采样值报文的研究也越来越重要。我国智能变电站系统结构与IEC61850标准相同,IEC61850使得变电站自动化系统(Substation Automation Systems,SAS)使用电子式互感器,并通过以太网将电压和电流量测值作为采样值(Sampled Measured Values,SMV)报文进行传送。然而SMV过高的传输率将会引起丢包等现象;同时SMV严格的4ms约束使得加密几乎不可实现。为此,本文首先分析了IEC61850可能面临的安全威胁,给出了可能的攻击类型。然后提出了一种虚假SMV报文的在线检测方案,同时也满足IEC61850规定的4ms约束。为了确保SAS的可靠运行,本文也提出了一种利用时序神经网络来对丢包进行检测的方法。为了验证本文所提方法的有效性,在合并单元和智能电子设备中进行验证。实验结果表明,通过对SMV报文丢包的准确预测,可以实现虚假数据的辨识并提高继电保护的鲁棒性。
The sampled measured values of smart substation become more and more critical to the stability and resilience of the power system. With the advent of IEC 61850, contemporary Substation Automation Systems (SAS) are utilizing electronic instrument transformers and merging units to transmit current and voltage measurements over Ethernet as Sampled Measured Values (SMV). However, if a substation’s network resources are not properly managed, the high transmission rate of SMV would make them prone to packet loss. Also, the strict 4ms time constraint imposed on SMVs makes encrypting these messages nearly impossible. Therefore, this paper first analyses the possible attacks to the IEC61850 protocol and then presents an online device level fake data detection system for detecting fake SMV messages without violating the 4ms time constraint set forth by IEC 61850. In order to ensure a reliable SAS operation, a coupled neural network - time series method for forecasting lost SMV packets is proposed. The proposed algorithm is implemented in a system composed of merging units and intelligent electronic devices developed for this purpose. Real-time experimental results of the proposed algorithms over a real IEC 61850 network showe excellent results in terms of detecting fake messages and increasing the robustness of protection schemes by accurately forecasting dropped SMV packets.
Techniques of Automation and Applications
sampled measured value
false data injection
lost packet forecasting