Statistical and Intertemporal Methods Using Embeddings for Non-linear AC Power System State Estimation
2018-11-14T19:50:03Z (GMT) by
This thesis aims to improve the robustness of state estimation (SE) methods currently used by the electric<br>power industry. The main objective of today’s SE is to estimate system state using redundant measurements<br>in the presence of bad data and constantly varying network topology. The hoped-for of this thesis contributions<br>concerns the use of embedding techniques to overcome problems related to non-linearities and<br>uncertainties.<br>First, in order to improve static SE when reliable historical data is unavailable, we reformulate the leastsquares<br>non-convex static SE currently used as a convex optimization problem by using an embedding<br>and convex relaxation techniques. We demonstrate a significant improvement in accuracy, particularly in<br>reactive power/voltage estimates. We propose that the added computational complexity caused by this embedding<br>can be managed by implementing the method as a distributed algorithm developed on the basis of<br>an underlying power system graph.<br>On the other hand, when reliable historical data is available, this thesis proposes utilizing them by using<br>both static and dynamic data-driven state estimation methods. The static estimator uses the an embedding<br>of the current measurements and learns the state estimator using similar measurement-state pairs in the historical<br>records. Several speedup techniques from machine learning are proposed for overcoming the initial<br>computational complexity of the proposed method and making it potentially useful for online applications.<br>A dynamic data-driven state estimator requires a much faster sampling of the historical data to capture<br>dynamics. An expectation maximization algorithm for SE is proposed for learning in embedded state space.<br>Finally, the uncertainties in SE caused by a massive number of distributed energy resources motivate a<br>fully distributed probabilistic state estimation method. This thesis provides a Bayesian Network solution to<br>the SE problem in that setting. The proposed method employs a probabilistic graphical model and embeds<br>it in a ceratin probability space, which allow the use of a variational belief propagation method that is both<br>scalable and exact for tree networks in distribution systems.<br>To assess the improved performance achieved by applying the proposed methods in this thesis, we compare<br>them with currently used methods in various simulation scenarios. Using the results, a flow chart is<br>presented to determine which methods to apply in different scenarios.