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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. Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights

   https://doi.org/10.1109/ISGTEUROPE62998.2024.10863452  

   Shi, Di; Zhang, Qiang; Hong, Mingguo; Wang, Fengyu; Maslennikov, Slava; Luo, Xiaochuan; Chen, Yize (October 2024, IEEE)

   Deep reinforcement learning (DRL) holds significant promise for managing voltage control challenges in simulated power grid environments. However, its real-world application in power system operations remains underexplored. This study rigorously evaluates DRL’s performance and limitations within actual operational contexts by utilizing detailed experiments across the IEEE 14-bus system, Illinois 200-bus system, and the ISO New England node-breaker model. Our analysis critically assesses DRL’s effectiveness for grid control from a system operator's perspective, identifying specific performance bottlenecks. The findings provide actionable insights that highlight the necessity of advancing AI technologies to effectively address the growing complexities of modern power systems. This research underscores the vital role of DRL in enhancing grid management and reliability. 

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3. Energy management algorithm for resilient controlled delivery grids

   https://doi.org/10.1109/IAS.2017.8101777  

   Saleh, Mahmoud; Esa, Yusef; Mohamed, Ahmed A.; Grebel, Haim; Rojas-Cessa, Roberto (October 2017, 2017 IEEE Industry Applications Society Annual Meeting)

   Resilience of the power grid is most challenged at power blackouts since the issues that led to it may not be fully resolved by the time the power is back. In this paper, a Real-Time Energy Management Algorithm (RTEMA) has been developed to increase the resilience of power systems based on the controlled delivery grid (CDG) concept. In a CDG, loads communicate with a central controller, periodically sending requests for power. The central controller runs an algorithm, based on which it may decide whether to grant the requested energy fully or partially. Therefore, the CDG limits loads discretionary access to electric energy until all problems are resolved. The developed algorithm aims at granting most or all of the requested loads, while maintaining the health of the power system (i.e. the voltage at each bus, and the line loading are within acceptable limits), and minimizing the overall losses. An IEEE 30-bus standard Test Case, encountering a blackout condition, with high penetration of microgrids, has been used to test the developed algorithm. Results proved that the developed algorithm with the CDG have the potential to substantially increase the resilience of power systems. 

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4. A Framework to Evaluate PMU Networks for Resiliency Under Network Failure Conditions

   https://doi.org/10.1109/SmartGridComm52983.2022.9961035  

   Raj, Reuben Samson; Jin, Dong (October 2022, IEEE SmartGridComm, October 2022)

   Phasor Measurement Units (PMU), due to their capability for providing highly precise and time-synchronized measurements of synchrophasors, have now become indispensable in wide area monitoring of power-grid systems. Successful and reliable delivery of synchrophasor packets from the PMUs to the Phasor Data Concentrators (PDCs) and beyond, requires a backbone communication network that is robust and resilient to failures. These networks are vulnerable to a range of failures that include cyber-attacks, system or device level outages and link failures. In this paper, we present a framework to evaluate the resilience of a PMU network in the context of link failures. We model the PMU network as a connected graph and link failures as edges being removed from the graph. Our approach, inspired by model checking methods, involves exhaustively checking the reachability of PMU nodes to PDC nodes, for all possible combinations of link failures, given an expected number of links fail simultaneously. Using the IEEE 14-bus system, we illustrate the construction of the graph model and the solution design. Finally, a comparative evaluation on how adding redundant links to the network improves the Power System Observability, is performed on the IEEE 118 bus-system. 

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5. Heterogeneous reinforcement learning for defending power grids against attacks

   https://doi.org/10.1063/5.0216874  

   Moradi, Mohammadamin; Panahi, Shirin; Zhai, Zheng-Meng; Weng, Yang; Dirkman, John; Lai, Ying-Cheng (June 2024, APL Machine Learning)

   Reinforcement learning (RL) has been employed to devise the best course of actions in defending the critical infrastructures, such as power networks against cyberattacks. Nonetheless, even in the case of the smallest power grids, the action space of RL experiences exponential growth, rendering efficient exploration by the RL agent practically unattainable. The current RL algorithms tailored to power grids are generally not suited when the state-action space size becomes large, despite trade-offs. We address the large action-space problem for power grid security by exploiting temporal graph convolutional neural networks (TGCNs) to develop a parallel but heterogeneous RL framework. In particular, we divide the action space into smaller subspaces, each explored by an RL agent. How to efficiently organize the spatiotemporal action sequences then becomes a great challenge. We invoke TGCN to meet this challenge by accurately predicting the performance of each individual RL agent in the event of an attack. The top performing agent is selected, resulting in the optimal sequence of actions. First, we investigate the action-space size comparison for IEEE 5-bus and 14-bus systems. Furthermore, we use IEEE 14-bus and IEEE 118-bus systems coupled with the Grid2Op platform to illustrate the performance and action division influence on training times and grid survival rates using both deep Q-learning and Soft Actor Critic trained agents and Grid2Op default greedy agents. Our TGCN framework provides a computationally reasonable approach for generating the best course of actions to defend cyber physical systems against attacks. 

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