Data-driven stability assessment and control of modern power systems - Li, Qiaoqiao (Nanyang Technological University, 2023)
Modern power systems are pushed to operate close to their secure limits due to increased system complexity and high penetration of renewable energy sources (RES). Conventional stability assessment (SA) and control methods face significant adaptiveness and robustness issues under highly uncertain and variable modern power systems. In recent years, with advanced real-time system monitoring and communication techniques like phasor measurement unit (PMU) devices, huge volumes of network-wide time-synchronized measurements can be collected and utilized. Under this new reality, data-driven methods have been identified as powerful tools for online and real-time stability assessment and control. This Ph.D. research aims to develop data-analytic methodologies online and real-time assess the system stability status and provide intelligent control actions when the power system is subject to a severe contingency. Specifically, this research covers a series of new methods in pre-fault SA, post-fault real-time SA and power system emergency control. Special efforts are devoted to enhancing the accuracy and robustness of data-driven methods under some extreme scenarios and practical problems, and a confidence-aware data-driven control scheme to manage the potential prediction error better. In data-driven SA, the point and consecutive data missing at both offline and online stages are studied, and two missing data-robust methods are developed, respectively for pre-fault SA and post-fault SA. For the pre-fault method, to enhance the robustness of SA against incomplete PMU measurements, a hybrid ensemble learning-based online SA method is proposed with feature-validation-based missing data estimators. Considering the possibility that some features may have a low correlation with other measurements, a feature validation process detects the potentially inaccurately estimated features to ensure the model performance and data restoration quality. Further, a novel spatial-temporal recurrent imputation network, SRIN, is proposed for time-series data missing at both sensor and PMU levels. The proposed SRIN method jointly learns the imputation for missing values and prediction output through an integrated multi-task recurrent network model, where the imputed values and the SA output can be simultaneously and effectively updated during backpropagation. In this way, the proposed method can be effectively trained by incompletely recorded datasets, which is immensely valuable for practical applications without high-quality historical data. Moreover, by learning shared representations or features across tasks, multi-task models can better exploit the information present in the data and improve the overall performance of both tasks. In addition, considering the time-varying and location-varying missing data patterns, the imputations of SRIN are derived by an intelligent combination of history-based and feature-based estimations through a trainable weighting component. Thereby, the proposed method is highly adaptive to various data missing scenarios, including both spatial and temporal consecutive missing patterns. In emergency control, data-driven methods for event-based load shedding (ELS) and response-based load shedding (RLS) are developed. Specifically, a hierarchical data-driven method is proposed for real-time prediction of ELS to mitigate fault-induced delayed voltage recovery (FIDVR). First, a novel adaptive trajectory sensitivity analysis is developed to optimize the ELS solutions offline and construct the ELS database. Then, by hierarchically modelling the ELS problem as a multi-output classification subproblem and a regression subproblem, the critical load buses mainly responsible for the delayed voltage recovery can be identified, and the over-shedding buses can be effectively reduced. Moreover, based on an improved weighted kernel extreme learning machine (WKELM) method, the direct mapping between system pre-fault operating conditions and corresponding control variables is constructed. Meanwhile, the established weighted learning scheme can extract the decision-making knowledge from the highly sparse and imbalanced dataset and emphasize on the rare yet critical cases. Therefore, the risk of insufficient load shedding is reduced, and better ELS effectiveness can be achieved. Subsequently, a probabilistic confidence-aware method for real-time RLS is developed to manage the potential prediction error better. The method is based on a two-stage process where a scalable Gaussian process (SGP) method determines the average load shedding amount at the first stage. The upper bound of the prediction interval is considered supplementary in the second stage. In parallel, if an RLS case is identified as unconfident, the conservative rule-based load shedding is taken as a backup plan rather than adopting LS estimations that cannot be trusted. Guided by the probabilistic information, the potential prediction error can be fully considered, and the confidence information can be incorporated into the control process, thereby more accurate and reliable load shedding actions can be taken. Leveraging confident predictions-based 2-stage RLS process, the excessive load loss can be reduced while ensuring high control effectiveness. All the proposed methods have been well verified on simulation experiments over standard power system models.