Learning the Unobservable: High-Resolution State Estimation via Deep Learning
Learning the Unobservable: High-Resolution State Estimation via Deep Learning
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DOI：
10.1109/allerton.2019.8919782
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Kursat Rasim Mestav;L. Tong
中科院分区：
文献类型：
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作者：
Kursat Rasim Mestav;L. Tong
考虑到有限部署相量测量单元的电力系统的快速限制状态估计的问题。开发了一种深入的神经网络结构，该体系结构整合了坏数据检测，数据清洁和最小平方误差状态估计。它包括通用的坏数据检测和贝叶斯州估计子网。提出了一种新型的通用坏数据检测技术，该技术不需要在常规和不规则操作条件下了解数据分布。通用坏数据检测的子网由一个反生成模型和巧合测试组成。它是通过使用慢速历史数据的生成对手网络和自动编码器来实现的。贝叶斯州估计子网络通过具有嵌入式电力系统的物理模型的生成对手网络进行培训。与常规加权最小二乘方法进行状态估计相比，提出的最小于点误差状态估计器不需要可观察到。模拟显示了/ver最先进的解决方案的估计准确性和在线计算成本的数量级提高。
The problem of achieving the fast-timescale state estimation for a power system with limited deployment of phasor measurement units is considered. A deep neural-network architecture that integrates bad-data detection, data cleansing, and the minimum mean squared error state estimation is developed. It includes a universal bad-data detection and a Bayesian state estimation subnetworks. A novel universal bad-data detection technique is proposed that requires no knowledge about data distributions under regular and irregular operating conditions. The subnetwork for universal bad-data detection consists of an inverse generative model and a coincidence test. It is implemented through the training of a generative adversary network and an auto-encoder using slow-timescale historical data. The Bayesian state estimation subnetwork is trained through a generative adversary network with embedded physical models of the power system. Comparing with the conventional weighted least squares approach to state estimation, the proposed minimum mean-squared error state estimator does not require observability. Simulations demonstrate orders of magnitude improvement in estimation accuracy and online computation costs of/ver the state-of-the-art solutions.