Smart grids promise a more reliable, efficient, economically viable, and environment-friendly electricity infrastructure for the future. State estimation in smart grids plays a pivotal role in system monitoring, reliable operation, automation, and grid stabilization. However, the power consumption data collected from the users during state estimation can be privacy-sensitive. Furthermore, the topology of the grid can be exploited by malicious entities during state estimation to launch attacks without getting detected. Motivated by the essence of a secure state estimation process, we consider a weighted-least-squares estimation carried out batch-wise at repeated intervals, where the resource-constrained clients utilize a malicious cloud for computation services. We propose a secure masking protocol based on data obfuscation that is computationally efficient and successfully verifiable in the presence of a malicious adversary. Simulation results show that the state estimates calculated from the original and obfuscated dataset are exactly the same while demonstrating a high level of obscurity between the original and the obfuscated dataset both in time and frequency domain.

中文翻译：

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基于数据混淆的智能电网状态估计期间保护电网拓扑和用户消耗模式

智能电网有望为未来提供更可靠，高效，经济可行和环境友好的电力基础设施。智能电网中的状态估计在系统监控，可靠运行，自动化和电网稳定中起着关键作用。但是，在状态估计期间从用户收集的功耗数据可能对隐私敏感。此外，恶意实体可以在状态估计期间利用网格的拓扑来发起攻击而不会被检测到。基于安全状态估计过程的本质，我们考虑了在重复的时间间隔内分批执行的加权最小二乘估计，其中资源受限的客户端利用恶意云提供计算服务。我们提出了一种基于数据混淆的安全屏蔽协议，该协议计算效率高且在存在恶意对手的情况下可以成功验证。仿真结果表明，从原始数据集和模糊数据集计算出的状态估计值完全相同，同时在时域和频域上都显示出原始数据集和模糊数据集之间的高度模糊性。