This work develops a distributed proportional-integral (PI) controller combined with the signal-to-interference-plus-noise ratio (SINR)-dependent dynamic event-triggered (DET) communication strategy to cope with the frequency regulation and power sharing problems subject to jamming attacks in an inverter-based islanded microgrid. In order to simplify the controller structure, power sharing and frequency restoration can be implemented simultaneously in the designed secondary control layer instead of the traditional hierarchical control implementation. Besides, the dynamic event-triggered communication strategy configured with each secondary controller can adaptively adjust the triggered threshold based on the SINR signal, which can reduce the congestion of communication networks caused by jamming attacks and ensure system control performance. A cyber-physical microgrid testbed is built based on the real-time simulator, OPAL-RT, and network simulation software, EXata, to verify the effectiveness of the proposed SINR-dependent event-triggered based distributed secondary controller.

This paper presents an adaptive stochastic spectral embedding (ASSE) method to solve the probabilistic AC optimal power flow (AC-OPF), a critical aspect of power system operation. The proposed method can efficiently and accurately estimate the probabilistic characteristics of AC-OPF solutions. An adaptive domain partition strategy and expansion coefficient calculation algorithm are integrated to enhance its performance. Numerical studies on a 9-bus system demonstrate that the proposed ASSE method offers accurate and fast evaluations compared to the Monte Carlo simulations. A comparison with a sparse polynomial chaos expansion method, an existing surrogate model, further demonstrates its efficacy in accurately assessing the responses with strongly local behaviors.

Although several multi-agent deep reinforcement learning (MADRL) algorithms have been employed in power distribution networks configured with high penetration level of Photovoltaic (PV) generators for active voltage control (AVC), the impact of the voltage fluctuation of a single PV node on voltage violations of other PV nodes in the network is ignored. Consequently, it leads to the conservativeness of the existing MADRL based AVC algorithms. In this paper, a robust MADRL control algorithm is designed to minimize the nodal voltage violation and line loss with the exploration of coupling voltage fluctuations across all the controlled nodes by coordinating PV inverters, and a physics factor is utilized to guide (physics-guided) the training policy with the expectation of a better performance compared to existing purely data-driven methods. In the proposed physics-guided multi-agent adversarial twin delayed deep deterministic (PG-MA2TD3) policy gradient algorithm, a physics factor, global sensitivity of voltage (GSV), is properly embedded in the algorithm to measure the influence of the nodal voltage fluctuation on voltage violations on the other controlled nodes with PV inverters and this GSV is shared in the learning center to guide the centralized learning and decentralized execution process. The multi-agent adversarial learning (MAAL) embedded with the GSV to seek an adaptive descend gradient for reducing the Q-value function appropriately rather than always assuming the worst case. Therefore, this physics-guided method can reduce the conservation and provide significantly better reward. Finally, the proposed algorithm is compared with several other methods on IEEE 33-bus, 141-bus and 322-bus with three-year data in Portuguese and the results indicate the proposed method can obtain the minimal voltage fluctuation and the best reward in the comparisons.

This paper studies the impacts of stochastic load fluctuations, namely the fluctuation intensity and the changing speed of load power, on the size of the voltage stability margin. To this end, Stochastic Differential-Algebraic Equations (SDAEs) are used to model the stochastic load variation; bifurcation analysis is carried out to explain the influence of stochasticity. Numerical study and Monte Carlo simulations on the IEEE 14-bus system demonstrate that a larger fluctuation intensity or a slower load power changing speed may lead to a smaller voltage stability margin. Particularly, this work may represent the first attempt to reveal the influence of the time evolution property of the driving parameters on the voltage stability margin in power systems.

In this paper, a hybrid measurement and model-based method is proposed which can estimate the dynamic state Jacobian matrix in near real-time. The proposed method is computationally efficient and robust to the variation of network topology. A numerical example is given to show that the proposed method is able to provide good estimation for the dynamic state Jacobian matrix and is superior to the model-based method under undetectable network topology change. The proposed method may also help identify big discrepancy in the assumed network model.

To enhance the computational efficiency of dynamic stability analysis, the Quasi Steady-State (QSS) model was proposed, yet it was later shown that the QSS model might fail to capture instability of the complete dynamic model. To deal with the issue, a hybrid QSS model has been proposed to ensure both the accuracy and efficiency of dynamic stability analysis. In this paper, we show that the failure of the QSS model provides important insights on designing control such that an intuitive yet effective emergency control is designed and integrated into the hybrid QS model to delay the occurrence of Hopf bifurcation and hence make the system oscillation-free.

Multivariate timeseries become a popular data form to represent images, that are used as suitable inputs to higher-level recognition processes. We present a novel cluster analysis based on timeseries structure to identify similar human motion sequenc

This paper proposes an approach called ‘structure-based spectral clustering’ to identify clusters in motion time series for sequential pattern discovery. The proposed approach deploys a ‘statistical feature-based distance computation’ for spectral clustering algorithm. Compared to traditional spectral clustering approaches, in which the similarity matrix is constructed from the original data points by applying some similarity functions, the proposed approach builds the matrix based on a finite set of feature vectors. When the proposed approach uses less data points and simpler similarity function to computing the similarity matrix input for spectral clustering, it can improve the computational efficiency in constructing the similarity graph in spectral clustering compared to conventional approach. Promising experimental results with high accuracy on real world data sets demonstrate the capability and effectiveness of the proposed approach for pattern discovery in motion video sequences.

We propose a new method for clustering multivariate time series. A univariate time series can be represented by a fixed-length vector whose components are statistical features of the time series, capturing the global structure. These descriptive vectors, one for each component of the multivariate time series, are concatenated, before being clustered using a standard fast clustering algorithm such as k-means or hierarchical clustering. Such statistical feature extraction also serves as a dimension-reduction procedure for multivariate time series. We demonstrate the effectiveness and simplicity of our proposed method by clustering human motion sequences: dynamic and high-dimensional multivariate time series. The proposed method based on univariate time series structure and statistical metrics provides a novel, yet simple and flexible way to cluster multivariate time series data efficiently with promising accuracy. The success of our method on the case study suggests that clustering may be a valuable addition to the tools available for human motion pattern recognition research.