The online monitoring data in distribution networks contain rich information on the running states of the networks. By leveraging the data, this paper proposes a spatio-temporal correlation analysis approach for anomaly detection and location in distribution networks. First, spatio-temporal matrix for each feeder line in a distribution network is formulated and the spectrum of its covariance matrix is analyzed. The spectrum is complex and exhibits two aspects: 1) bulk, which arises from random noise or fluctuations and 2) spikes, which represents factors caused by anomaly signals or fault disturbances. Then, by connecting the estimation of the number of factors to the limiting empirical spectral density of covariance matrices of residuals, the spatio-temporal parameters are accurately estimated, during which free random variable techniques are used. Based on the estimators, anomaly indicators are designed to detect and locate the anomalies by exploring the variations of spatio-temporal correlations in the data. The proposed approach is sensitive to the anomalies and robust to random fluctuations, which makes it possible for detecting early anomalies and reducing false alarming rate. Case studies on both synthetic data and real-world online monitoring data verify the effectiveness and advantages of the proposed approach.

Cyber, especially false data injection attacks (FDIAs), are gradually becoming a kind of severe threat to modern power grids. This paper proposes a blind FDIA approach against the state estimation of power grids based on matrix reconstruction and subspace estimation. As a blind FDIA method, it requires no information about the system parameters and topology, but only needs a limited period of measurement data. In particular, this paper investigates the interference of random measurement noise for subspace based blind FDIA methods, and designs a scheme to alleviate the influence of this random noise in the process of FDIA. Compared with previous blind FDIA methods, the proposed method overcomes the shortcoming that measurement noise is difficult to address when data is not abundant. Thus the presented approach performs high successful rate of FDIA, and it is able to operate when measurement data is very limited. Besides, it performs great robustness to large-scale power grids and high-level measurement noise. Numerous cases demonstrate the effectiveness and advantages of the proposed method.

Hybrid ac/dc microgrids (HMGs) have emerged as a promising paradigm for integrating large numbers of inherently uncertain and correlated renewable energy sources (RESs). To address the uncertainty introduced by extensive RES integration, a quad-level energy management model is proposed for HMGs, incorporating a novel data-driven uncertainty set. Specifically, a pair convex hull uncertainty set (PCHUS) is developed with adjustable size, which utilizes a graph neural network to identify and exclude outliers in RES data. This approach provides trustworthy RES data at any given confidence level. Then, a quad-level energy management model is designed to determine the minimal-size PCHUS among all possible options, ensuring the least conservative solution, while maintaining robustness against RES fluctuations. Furthermore, a modified version of Taguchi’s orthogonal array testing (TOAT) method, termed quasi-TOAT, enhances the proposed solution algorithm. This modification enables parallel processing capabilities, significantly improving both the global optimum-seeking process and computational efficiency. To validate the proposed approach, the proposed uncertainty set and enhanced algorithm are compared against existing methods using a practical HMG case study. The results demonstrate the effectiveness and superiority of the proposed methodology in managing uncertainty within HMGs.

In AC microgrids (MGs), the required high control gain for dealing with control faults would reduce the robustness of the secondary control to communication constraints. To tackle with this problem, the subarea physical infrastructure of AC MGs and the two-layer secondary control framework are designed. For the physical infrastructure, the whole AC MG is divided into several areas, where each area is composed of a distributed generator (DG) and several geographical-close loads. Furthermore, by utilizing the advanced metering infrastructure (AMI), loads transmit the local load change information to the DG in the same area. Based on this physical infrastructure, a distributed event-triggered fixed-time fault-tolerant (ET-FTFT) secondary control framework is proposed in this paper to improve the resilience of the secondary control to sensor and actuator faults/attacks, and communication constraints, simultaneously. The adopted two-layer control framework is to decouple the communication constraints and control faults. The event-based strategy is adopted in the upper layer to reduce the communication burden and Zeno behavior can be evaded. Real-time simulations based on the NI-PXI real-time simulator validate the advantages of the distributed ET-FTFT secondary control framework, which are the better robustness to control faults, and the fixed-time convergence to improve the power quality.

Recently, many distributed fault-tolerant (Di-FT) secondary controls have been designed to improve the robustness of the islanded AC microgrids (MGs) to actuator faults/attacks. However, some problems still exist among the existing research: (a) the convergence speed of the faulty MG's states has not been discussed, which is important for reducing the inter-harmonic component around the fundamental frequency thereby improving the power quality; (b) large computing and sampling resources are required by the relatively high control gain for dealing with the possible serious actuator faults. Therefore, a distributed event-triggered fixed-time (Di-ET-FT) fault-tolerant secondary control of the faulty islanded AC MG is designed in this paper to release the communication burden, save the computing resources, and achieve the fixed-time convergence speed for improving power quality simultaneously. The Di-ET-FT fault-tolerant secondary control has a two-layer control framework, which are the observer layer as the upper layer and the fault-tolerant control layer as the lower layer. Meanwhile, two event-triggered strategies are designed in these two layers to decrease sample/control updating numbers and communication numbers, respectively. Zeno behavior of the Di-ET-FT fault-tolerant secondary control can be evaded effectively by proving that the interevent time is lower bounded by a positive value. The abovementioned advantages of the designed secondary control are validated by the real-time simulation based on NI-PXI simulator.