Transient event identification is essential for power system operation and situational awareness. The increased penetration of the high sampling frequency phasor measurement units (PMUs) enables using PMU data to analyze power system events, and thus enhances power system visualization, monitoring and control. At the same time, the risks associated with cyberattacks on power systems increase. A malicious cyberattack on PMUs, aiming at generating fake transient data, may lead to incorrect actions that jeopardize system reliability. Therefore, it is critical to distinguish between fake data and real data when analyzing transient events. Utilizing PMU measurements, this article develops a data-driven approach, based on text-mining methodologies, for classifying transient events and identifying fake events caused by false data attacks. The developed methodology provides credible information regarding the cause of various events, and facilitates postevent decision-making to prevent potential cascading failures. Case studies, performed on the IEEE 30-bus and IEEE 118-bus systems, show that the developed approach is efficient in classifying false data and identifying different transient events regardless of the system topology, loading conditions, or the placement of PMUs.

An effective model to represent real-world wind power production scenarios is essential for an accurate assessment of the impact of wind power generation on power systems. Such a model should capture the spatial and temporal correlations between different wind turbines that are part of a wind farm. Accurate modeling of these correlations will ensure that the wind power uncertainty can be properly quantified. This is especially critical when analyzing systems with high penetration of wind power productions. This paper addresses this critical need by developing the Hourly Mixed Copula Model, a new probabilistic model, based on Gaussian and Archimedean copulas, to model the uncertainty and variability of wind power generation. This model enables the simulation of realistic scenarios of a wind farm power production. Metrics such as the Euclidean distance and Kullback-Leibler divergence are used to demonstrate the effectiveness of the developed model in incorporating the spatio-temporal dependence among the turbine power productions. The impact of accurate characterization of uncertainty in wind power modeling on power system steady-state operational planning is also demonstrated by case studies. These studies indicate that, in order to correctly evaluate the effect of wind power variation on steady-state operational planning, the dependence among wind power resources should not be neglected for power flow studies.

A large volume of data is typically needed to achieve an accurate solar generation prediction. However, not all types of data are consistently available. Various research efforts have addressed this challenge by developing methods that identify the most relevant features for predicting solar generation. However, the optimal features vary with different weather patterns, making it impossible to select a fixed set of optimal features for all weather patterns. This study develops a new framework to accurately predict solar irradiance using dynamically changing optimal features. The developed model first incorporates feature extraction with clustering techniques to identify representative weather data from a dataset. Next, using deep reinforcement learning (DRL), a new feature selection method is developed to yield the minimum features required to accurately forecast solar irradiance from representative data. Benefiting from the model-free nature of DRL, the developed method is adaptive to various weather conditions, and dynamically alters the selected features. Case studies using real-world data have shown that the developed model significantly reduces the volume of data required for accurate irradiance forecasting for different weather patterns.

Effective mitigation of thermal overloads is crucial in preventing power grid outages. With large-scale adoption of phasor measurement units, new and more effective mitigation opportunities have emerged. However, the possibility of false data attacks on the measured data, or the relay status information, threatens the promise of utilizing measurements for timely mitigation of thermal overloads. False data can lead to wrong estimation of the system states and the power flow model, which may eventually lead to wrong mitigations. To address these newly introduced challenges, in this article, a data-driven model-predictive control method is introduced for mitigation of the thermal overloads, which is resilient to false data injection attacks. This is achieved by constructing a data-driven power flow model from the trustworthy system measurements, which is independent from system topology and model parameters. Hence, the power flow model becomes immune to wrong system model information. To eliminate the adverse impacts of false data on the measurements, the actual system states are recovered from the historical trustworthy data if an attack is detected. Case studies demonstrate that the developed model predictive control methodology effectively identifies the optimal mitigations in a finite prediction horizon.

Controlled islanding is a last-resort recovery scheme for preventing cascading failures. Although effective, high levels of uncertainty from renewable energy resources challenge the promise of controlled islanding to prevent additional failures. Such uncertainties can lead to subsequent failures in individual islands, render the overall recovery approach ineffective, and reduce the grid resilience. This paper develops a stochastic cascading failure model to assess the resilience of islands against cascading failures under high penetration of renewables. The stochastic model incorporates physical system constraints and system data to predict the failures. Hence, the most probable cascades in islands with high uncertainties are efficiently identified, which enables evaluating the islands' resilience to cascades. The developed model significantly improves the efficiency and accuracy of the resilience study in the presence of uncertainties. Case studies indicate that the increased penetration of intermittent renewables can improve or degrade the system resilience depending on the level of renewable generation. The case studies also reveal that an islanding strategy to minimize the imbalance of load and generation can lead to less resilient islands. These findings substantially enhance the understanding of controlled islanding under high generation uncertainties and improve resilience-oriented grid planning.

Probabilistic solar generation forecasting provides a better means of quantifying generation uncertainties for power grid operations by providing a range of potential power outputs rather than a single-point estimate. The traditional probabilistic models are unreliable under rapidly changing weather conditions due to fluctuating data correlations, necessitating dynamic modeling of spatio-temporal feature correlations under diverse weather scenarios. The correlations represent the interactions across space and time that reflect the impact of weather conditions on solar power output. This paper addresses this critical problem with a novel method by fusing copula theory and machine learning methods to dynamically quantify the spatio-temporal correlations among meteorological data under diverse weather conditions. The meteorological data and the functions employed to estimate spatio-temporal correlations change dynamically based on weather conditions. A data-driven environment-aware model has been developed to produce probabilistic forecasts from this data, effectively quantifying uncertainty in meteorological data. Case studies on real-world datasets demonstrate that the proposed dynamic method exhibits robust performance in solar irradiance and solar power forecasting. Moreover, the model outperforms state-of-the-art models by up to 60% higher accuracy under non-sunny conditions in autumn and winter.