This work addresses the secondary control issues in microgrid for frequency and voltage restoration under network threats. Firstly, a distributed control protocol is constructed using the consensus method of multi-agent system (MAS). Secondly, sliding mode control (SMC) using an improved stretchy event-triggered mechanism (ISETM) is designed to reduce data transmission. By employing Acknowledgment (ACK) mechanisms, ACK-based self-recovery (ASR) strategy is developed as an attack detection and compensation scheme to improve control performance under unsecured communication environment. Then, sufficient conditions are derived using Lyapunov stability theory to ensure a finite time convergence on the sliding mode surface. Finally, a test system consisting of 20 DGs is applied to assess the scalability of this distributed control algorithm. Experiment tests on a hardware-in-the-loop (HIL) platform are conducted to verify that the designed control scheme offers advantage and efficiency on microgrid secondary control.

This paper investigates the frequency regulation problems of integrated renewable energy sources (RESs) power system under the non-ideal environment in open communication networks. Some problems are raised up: (i) The integration of high penetration levels RESs into the power system reduces the inertia characteristics and degrade frequency regulation performance. (ii) The non-ideal network environment disrupt data transmission and deteriorate control accuracy. To address the above problems, a load frequency control (LFC) with virtual synchronous generator method (VSG-LFC) is presented, which greatly reduces the power system’s sensitivity to the level of sustainable energy penetration. Then, a hybrid trigger mechanism (HTM) consisting of a time trigger mechanism and an improved adaptive event trigger mechanism is adopted to handle the unforeseen environment influence with less occupied network bandwidth. Meanwhile, theoretical analysis on the frequency stabilization of power system is conducted by using looped-functional method and the related knowledge. In simulation evaluation, the proposed HTM along with VSG-LFC scheme outperforms the convention LFC subject to the introducing of RESs. Specifically, the VSG-LFC scheme achieves reductions of 98.9892% in IAE, 98.9234% in ITAE, and 87.0486% in ISE compared to conventional LFC.

Heart is the most important organ of the human body, and Electrocardiogram (ECG) is an essential tool for clinical monitoring of heart health and detecting cardiovascular diseases. Automatic detection of ECG anomalies is of great significance and clinical value in healthcare. However, performing automatic anomaly detection for the ECG data is challenging because we not only need to accurately detect the anomalies but also need to provide clinically meaningful interpretation of the results. Existing works on automatic ECG anomaly detection either rely on hand-crafted designs of feature extraction algorithms which are typically too simple to deliver good performance, or deep learning for automatically extracting features, which is not interpretable.