High-fidelity information extraction in future power grids

Abstract

The recent decades have witnessed the rapid growth of the deployment of smart meters in power systems. The massive amount of data collected from smart meters provide rich information of power systems, such as system dynamics and load profiles. The reliable, high-fidelity data processing is beyond the capability of the existing data analytic tools of power system monitoring. The goal of this research aims to bridge the gap between power system monitoring and high dimensional data analysis. One of the key observations is that many data in power systems have intrinsically low dimensionality, although the original data are high dimensional. This dissertation focuses on high-fidelity information extraction by exploiting these low-dimensional structures. The first part of this dissertation studies one low-rank model, dictionary learning, with applications in energy disaggregation at the substation level (EDS). The high penetrations of renewable distributed energy resources, such as solar generations, are usually behind-the-meter (BTM) and thus are invisible to the power system operators. The uncertainty introduced by the BTM renewable generations brings great challenges to reliable power system planning and operation. This dissertation formulates the energy disaggregation tasks as a dictionary learning problem and obtains accurate estimations of load profiles from aggregate measurements. Because it is difficult to obtain full labels for training data, this dissertation, for the first time, addresses the “partial labels” issue in EDS and proposes a dictionary learning-based EDS method to disaggregate each load from aggregate measurements in real-time. In the offline training stage, a novel column-sparsity constraint is added by exploiting the group sparsity of unlabeled loads, and an incoherence regularization term is proposed to promote discriminative patterns for each load. In the online disaggregation stage, the traditional sparse decomposition approach is improved by decomposing the aggregate measurements as a linear combination of some representative learned disaggregations. Because of the invisibility and stochastic nature of BTM renewable generation, it is inevitable that the disaggregation results contain errors. However, existing EDS approaches cannot quantify the uncertainty of the disaggregation results. This dissertation then studies the EDS problem from a Bayesian perspective and models the EDS as a Bayesian dictionary learning problem. A scheme to measure the uncertainty of the disaggregation results is proposed. In the offline training stage, the proposed approach learns the probabilistic distributions of dictionaries and coefficients from the aggregate training data with partial labels. In the online disaggregation stage, the proposed approach computes the predictive mean and covariance of the probabilistic distribution of each load consumption. The mean is used as the load estimation andthe covariance is employed to provide the uncertainty measure of the disaggregation results. The second part of this dissertation studies another low-rank model, the robust matrix completion, with applications in synchrophasor data recovery. Phasor measurement units (PMUs) offer high-resolution synchrophasor measurements and thus provide better visibility of the power system dynamics. However, the synchrophasor data quality issues, such as missing data and bad data attacks, hinder the PMU data from being incorporated into the power system operation and control and prevent the large-scale deployment of PMU in North America.This dissertation formulates the synchrophasor data recovery problem as a Bayesian robust Hankel matrix completion problem. In particular, the proposed method exploits the low-rank Hankel property of synchrophasor data to recover the consecutive and simultaneous data loss/corruption, where the standard low-rank matrix completion methods cannot handle this extreme case. The proposed Bayesian method learns the probabilistic distributions of decomposed factors and infers the distribution of synchrophasor data. Compared with existing data recovery methods, this dissertation, for the first time, provides the uncertainty measure of the returned results. The low-rank Hankel approximation of the synchrophasor data matrix assumes that the data are generated from a linear dynamical system. When a significant event takes place in power systems, the nonlinear synchrophasor data do not hold the low-rank property. This dissertation then studies the lifted low-rank Hankel property of nonlinear synchrophasor data in a higher dimensional space by exploiting the kernel trick. The idea of the proposed nonlinear synchrophasor data recovery method is to lift the Hankel matrix of nonlinear synchrophasor data into a higher dimension such that the lifted Hankel matrix is low-rank in that high dimensional space. The kernel trick is employed to perform the implicit lifting. The proposed Bayesian framework can also provide an uncertainty index to measure the uncertainty level of the recovery results.