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Accurate event detection is critical for power system operators to take appropriate control strategies so as to ensure the system stability under disturbances. Whereas, most existing event detection techniques are conducted under a single analytical window and criterion, which leads to the misdetection for events of different dynamics. Given this background, a synchrophasor-based real-time event detection method using dual windowed recurrence quantification analysis (DWRQA) algorithm is proposed in this article. First, the recurrence quantification analysis algorithm for synchrophasor data is employed to analyze the nonstationary state of power systems, and a recurrence rate-based power system event indicator is presented to reveal the occurrence of power system events. Then, a real-time event detection scheme based on the dual window structure is proposed to monitor both fast and slow dynamic events of power systems. Finally, case studies on the Western Electricity Coordinating Council (WECC) 179-bus power system and the actual Guangdong power system (GDPS) are performed to demonstrate the effectiveness of the proposed method. Simulation results show that the proposed method can detect both fast and slow dynamic events without misdetections and false alarms, and short time delays for detecting events also make it suitable for real-time applications in actual power systems.

Accurate wind power forecast (WPF) is critical for ensuring secure and economic operation of a power system, and combination forecasting approaches for WPF have been proved effective on attaining accurate forecasting results. However, the weights in a combination forecasting model are usually predetermined based on the global performance on the training set, limiting the adaptability of the model to different scenarios. To further enhance the accuracy and effectiveness of the combination forecasting approaches, this article proposes an adaptive weighted combination forecasting approach based on the deep deterministic policy gradient (DDPG) so as to consider the local behavior accompanied by the change of external environment. Three sub-models are first selected considering the equal-likelihood and dispersion indices to construct a combination model. Then, the DDPG agent is trained to act as a weight generator by interacting with the environment and to maximize the reward. Thus, the DDPG agent can perceive the environmental changes online and dynamically weight the sub-models to attain accurate forecasting results. Case studies demonstrate that the forecasting accuracy of the proposed approach is better than that of all sub-models and several benchmark combination forecasting approaches.

This article presents a dynamic restoration strategy for active distribution networks (ADNs) by coordinating repair crew dispatch and frequency-constrained cold load pickup. To incorporate the stochastic repair time, the repair crew dispatch is formulated as “event-driven” with the implementation of model predictive control (MPC). The stochastic repair time is estimated, convexified, and updated dynamically with each MPC execution. The finish of a repair task triggers the subsequent cold load pickup model, where the frequency dynamics are computed and linearly constrained with the help of a uniform frequency response model for low-inertia systems. Next, a co-optimization framework of the two models is developed to coordinate the repair crew dispatch and cold load pickup under a unified time scale. Numerical results on a modified IEEE 33-node test feeder and a real-world 136-node distribution system have verified the effectiveness of the proposed model.

Forming microgrids (MG) in distribution systems (DSs) is an operational measure proposed to enhance power system flexibility. The computational performance of the applied optimization techniques varies greatly according to the constraints ensuring network radiality. Formulations of radiality constraints differ from each other by model complexity and the search space of the relaxed linear programming (LP) problem. In this paper, widely used formulations of radiality constraints are first revisited and flexibly enhanced to address the complex operating environment of modern DSs. To incorporate various radiality constraints and provide an optimization basis for comparison, a flexible MG formation problem is proposed with the ability to automatically allocate distributed generators (DGs) into multiple MGs. Combined formulations with parent-child (PC) constraints are then developed to shrink the LP feasible region. Moreover, to accelerate the branching process by applying the branch-and-cut (B&C) algorithm, an efficient formulation of radiality constraints is proposed by adding extra constraints that exclude fundamental loops into the single-commodity flow formulation. To further accelerate the branching process, a dynamic constraint generation algorithm is developed without substantially increasing the complexity of the LP problem. DSs of various complexities and sizes are employed to verify the computational efficiency of the proposed formulations. The paper concludes by suggesting the most efficient formulations of radiality constraints according to the scale of the targeted DS.