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1. Prognostic Health Monitoring of DC Microgrid with Fault Detection and Localization using Machine Learning Techniques

   https://doi.org/10.1109/ECCE53617.2023.10362739  

   Bohara, Bharat; Krishnamoorthy, Harish S. (October 2023, IEEE)

   DC microgrids incorporate several converters for distributed energy resources connected to different passive and active loads. The complex interactions between the converters and components and their potential failures can significantly affect the grids' resilience and health; hence, they must be continually assessed and monitored. This paper presents a machine learning-assisted prognostic health monitoring (PHM) and diagnosis approach, enabling progressive interactions between the converters at multiple nodes to dynamically examine the grid's (or micro-grid's) health in real time. By measuring the resulting impedance at the power converters' terminals at various grid nodes, a neural network-based classifier helps detect the grid's health condition and identify the potential fault-prone zones, along with the type and location of the fault type in the grid topology. For a faulty grid, a Naive Bayes and a support vector machine (SVM)-based classifiers are used to locate and identify the faulty type, respectively. A separate neural network-based regression model predicts the source power delivered and the loads at different terminals in a healthy grid network. The proposed concepts are supported by detailed analysis and simulation results in a simple four-terminal DC microgrid topology and a standard IEEE 5 Bus system. 

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2. Balancing Flying Capacitor Multilevel Converters With Coupled Inductors: Multi-Resonant Dynamics

   https://doi.org/10.1109/TPEL.2025.3526131  

   Zhou, Daniel H; Chen, Minjie (January 2025, IEEE Transactions on Power Electronics)

   This paper investigates dynamic balancing of flying capacitor multilevel (FCML) converters with coupled inductors. Coupled inductors help to reduce the ripple current, accelerate transient response, and balance the flying capacitors of FCML converters at steady-state. However, coupled inductors also change the dynamic balancing properties compared to uncoupled inductors, and these principles must be understood for robust design. As an extension of a previously developed feedback mechanism for understanding the steady-state behaviors of coupled inductors in FCML converters, this paper derives models of coupled inductor FCML converters in dynamic operation, revealing several key insights: (i) the multi-resonant behavior of large-order FCML converters and their dependence on the initial conditions, (ii) how power dissipation relates to balancing speed, and (iii) the relation between multiphase and multilevel FCML balancing. The insights uncovered by this paper can provide useful guidelines for designing multi-phase self-balanced FCML converters with coupled inductors. 

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3. Frequency Scan–Based Mitigation Approach of Subsynchronous Control Interaction in Type-3 Wind Turbines

   https://doi.org/10.3390/en14154626  

   Alatar, Faris; Mehrizi-Sani, Ali (August 2021, Energies)

   null (Ed.)

   Integration of wind energy resources into the grid creates several challenges for power system dynamics. More specifically, Type-3 wind turbines are susceptible to subsynchronous control interactions (SSCIs) when they become radially connected to a series-compensated transmission line. SSCIs can cause disruptions in power generation and can result in significant damage to wind farm (WF) components and equipment. This paper proposes an approach to mitigate SSCIs using an online frequency scan, with optimized phase angles of voltage harmonic injection to maintain steady-state operation, to modify the controllers or the operating conditions of the wind turbine. The proposed strategy is simulated in PSCAD/EMTDC software on the IEEE second benchmark model for subsynchronous resonance. Simulation results demonstrate the effectiveness of this strategy by ensuring oscillations do not grow. 

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4. Identification of Substation Configurations in Modern Power Systems using Artificial Intelligence

   Madurasinghe, Dulip; Venayagamoorthy, Ganesh K (July 2022, 11TH BULK POWER SYSTEMS DYNAMICS AND CONTROL SYMPOSIUM (IREP 2022))

   Power system transmission network topology is utilized in energy management system applications. Substation configurations are fundamental to transmission network topology processing. Modern power systems consisting of renewable energy sources require reliable and fast network topology processing due to the variable nature of wind and solar power plants. Currently used transmission network topology processing, which is based on the relay signals communicated through SCADA is not highly reliable or highly accurate. Substation configuration identification (SCI) for different substation arrangements including main and transfer bus arrangement (MTBA), ring bus arrangement (RBA), and single bus arrangement (SBA) is investigated. Synchrophasor measurement based SCI for functional arrangements (FA) using artificial intelligence (AI) approaches is proposed in this paper. This method improves monitoring FA. Typical results for MTBA, RBA and SBA substation configuration identification is presented. A modified two-area four-machine power system model with two grid connected solar PV plants consisting of MTBA, RBA and SBA is simulated on real-time digital simulator. AI based SCI is shown to accurately identify all possible FAs for the three substation arrangements under any operating condition. 

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5. A Graph Theoretic Approach to Power System Vulnerability Identification

   https://doi.org/10.1109/TPWRS.2020.3010476  

   Sen Biswas, Reetam; Pal, Anamitra; Werho, Trevor; Vittal, Vijay (July 2020, IEEE Transactions on Power Systems)

   During major power system disturbances, when multiple component outages occur in rapid succession, it becomes crucial to quickly identify the transmission interconnections that have limited power transfer capability. Understanding the impact of an outage on these critical interconnections (called saturated cut-sets) is important for enhancing situational awareness and taking correct actions. This paper proposes a new graph theoretic approach for analyzing whether a contingency will create a saturated cut-set in a meshed power network. A novel feature of the proposed algorithm is that it lowers the solution time significantly making the approach viable for real-time operations. It also indicates the minimum amount by which the power transfer through the critical interconnections should be reduced so that post-contingency saturation does not occur. Robustness of the proposed algorithm for enhanced situational awareness is demonstrated using the IEEE-118 bus system as well as a 17,000+ bus model of the Western Interconnection (WI). Comparisons made with different approaches for power system vulnerability assessment prove the utility of the proposed scheme for aiding power system operations during extreme exigencies. 

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