Line current differential relays (LCDRs) are measurement-driven relays that rely on time-synchronized multi-phase current waveforms to infer internal faults in AC and DC power networks. In inverter-based microgrids, however, the increasing reliance on digitally communicated measurements exposes LCDRs to false-data injection attacks (FDIAs), in which adversaries manipulate remote measurement streams to create protection-triggering yet physically inconsistent current trajectories. This paper addresses this emerging measurement integrity problem by introducing a measurement integrity validation scheme that operates as a supervisory instrumentation layer for modern LCDRs. The proposed scheme interprets short windows of synchronized instantaneous current measurements recorded during relay operation and assesses their physical consistency to distinguish genuine fault-induced trajectories from cyber-manipulated measurement streams. A recurrent neural network is trained offline using only relay-available current measurements and exploits the temporal structure of differential current waveforms, which remains informative in inverter-dominated systems where current magnitude is no longer a reliable observable. The method requires no additional sensors, auxiliary protection elements, or prior knowledge of network topology, and is applicable to both AC and DC LCDRs without structural modification. The proposed measurement validation scheme is evaluated on an islanded inverter-based microgrid under a comprehensive set of fault and FDIA scenarios, demonstrating high detection accuracy while preserving relay dependability. Hardware-in-the-loop validation using an OPAL-RT real-time simulator confirms that the scheme satisfies protection timing constraints and can operate in real time under realistic operating conditions.

翻译：线路电流差动继电器（LCDR）是测量驱动型继电器，依赖时间同步多相电流波形来推断交直流电网的内部故障。然而，在逆变器型微电网中，对数字通信测量的日益依赖使LCDR面临虚假数据注入攻击（FDIA）——攻击者操控远程测量流，生成触发保护但物理上不一致的电流轨迹。本文针对这一新兴的测量完整性问题，提出一种测量完整性验证方案，该方案作为现代LCDR的监督式仪表层运行。所提方案解析继电器动作期间记录的时间同步瞬时电流短时窗，评估其物理一致性，以区分真实故障引发的电流轨迹与网络操控的测量流。循环神经网络仅利用继电器可用的电流测量进行离线训练，并利用差动电流波形的时序结构（在电流幅值不再可靠可观测的逆变器主导系统中，该结构仍具信息量）。该方法无需额外传感器、辅助保护元件或网络拓扑先验知识，且适用于交直流LCDR且无需结构修改。在孤岛逆变型微电网的全面故障与FDIA场景下评估所提测量验证方案，结果表明其在保持继电器可靠性的同时实现了高检测精度。基于OPAL-RT实时仿真器的硬件在环验证证实，该方案满足保护时序约束，并能在实际运行条件下实时运行。