Automating the task of fault detection and diagnosis is essential for reducing the operational and maintenance costs of power electronic converters. Hence, this paper introduces an online optimization methodology for digital twins to diagnose multiple faults in grid connected inverters. In addition to enhancing accuracy, we channelize our efforts towards reducing computational complexity in parameter configuration under a limited set of training data. To address cybersecurity vulnerabilities during the training phase of our digital twin, we performdata sanitizationwith the aid of a quantitative association rule mining technique. For classification performance assessment, various fault cases in a virtual synchronous generator are considered to demonstrate the efficacy of our approach. Our research outcomes reveal increased accuracy and fidelity levels achieved by our digital-twin design, paving the way for reliable and trustworthy decision-making under anomalous conditions.

Effective current sharing among distributed generation (DG) units is crucial for microgrid stability, efficiency, and reliability. However, current sensors, which are essential for monitoring and control, might be subject to integrity cyberattacks. This paper develops a novel resilience-enhancing framework based on a decentralized adaptive architecture for current sharing in DC microgrids under integrity cyberattacks in current sensor measurements. The proposed framework employs two key components for each DG unit; a resilient unknown input observer (UIO) and a dynamic virtual current sensor. The proposed resilient UIO and virtual sensors estimate the correct current from inaccurate current sensor measurements and baseline control inputs in the presence of uncertain and unknown load conditions and power lines. Our proposed approach consists of a mathematically rigorous input-output stability analysis. It is shown that the interconnection of the proposed framework with microgrids’ baseline controllers enables DC microgrids to adapt to and compensate for sensor uncertainties without compromising overall performance. Specifically, the augmented framework with the baseline microgrid controller proposed guarantees asymptotic stability of the closed-loop dynamical system under time-invariant current sensor uncertainties and uniform ultimate boundedness under time-varying current sensor uncertainties. Several case studies on a DC microgrid illustrate the efficiency and performance of the proposed resilient current sharing scheme.

This paper presents a novel framework for intrusion detection specially designed for cyberattacks, such as Denial-of-Service, Distributed Denial-of-Service, Distributed Reflection Denial-of-Service, Brute Force, Botnets, and Sniffing, on vehicles that are situated in the Internet of Vehicles environment. We propose an intrusion detection system based on machine learning that is capable of detecting abnormal behavior by examining network traffic to find unusual data flows. In this paper, we have presented a strategy for intrusion detection through a careful evaluation and selection of the most effective techniques for the following steps of the machine learning process: (i) data preprocessing by using Z-score normalization that preserves the data distribution for the proposed method and handles outliers; (ii) feature selection by using a regression model that simplifies the model complexity and reduces the execution time; and (iii) model selection and training – Random Forest, Extreme Gradient Boosting, Categorical Boosting, Light Gradient Boosting Machine – with hyperparameter optimization to control the behavior in the training phase and to prevent overfitting. The effectiveness of the proposed solution is demonstrated by extensive numerical experiments carried out using the well-known standard datasets CIC-IDS-2017, CSE-CIC-IDS-2018, and CIC-DDoS-2019, both separately and merged. We achieved a high accuracy above 99.8% within a running time of 46.9 s and 0.24 s detection time for the three combined intrusion detection system datasets, thereby showing that the proposed intrusion detection system outperforms the previous methods introduced in the literature.

Dual active bridge (DAB) converters are becoming one of the most reliable interfaces due to their high voltage range, easy realization of zero voltage switching (ZVS), galvanic isolation, etc. To meet specific consumer requirements in DC microgrids, DAB converters operate with different topologies. In this paper, a finite control set model predictive control (FCS-MPC)-based power sharing control is proposed when DAB converters are in different structures which are input series-output series, input parallel-output parallel, input parallel-output series, and input parallel-output parallel connecting structures. By analyzing the power balance relationship of the input port and output port, it determines the variable (input voltage/current or output voltage/current) which should be controlled to realize the power sharing. And then the cost function is designed based on the control strategy. Besides, this paper proposes a Kalman filter based parameter estimation method for the DAB converter. In this case, it guarantees the robustness of the MPC algorithm. Finally, the simulation results prove the effectiveness of the proposed method.

This article investigates the impact of data integrity attacks (DIAs) on cooperative economic dispatch of distributed generators (DGs) in an ac microgrid. To establish resiliency against such attacks and ensure optimal operation, a localized event-driven attack-resilient scheme is proposed. Most of the existing works examine neighboring information to infer the presence of DIAs, where the detection is limited to events such as multiple link failures. Two kinds of DIAs are considered in this article-namely, fault and random attacks, which are segregated based on the final values of consensus updates. First, to improve the robustness of the detection theory, a localized resilient control update is designed by modeling each DG with a reference incremental cost. Second, event-driven control signal is generated for the local incremental cost and held upon the detection of attacks, to prevent malicious data from propagating to the neighboring nodes. The proposed strategy acts immediately upon the detection of DIA to ensure maximization in the economic profit. Furthermore, the proposed detection approach is theoretically verified and validated using simulation conditions.