arXiv subjects

Anurag K. Srivastava

Publications and source records attributed to Anurag K. Srivastava.

10 recordsLinked to original sources

arXiv2026

DynaTrust-VVC: Directional Physics-Informed Trust-Based Detection and Mitigation for Cyber-Resilient Multi-Agent Volt--VAR Control

Distributed Volt--VAR control relies on voltage measurements and is therefore vulnerable to false-data injection. Neighbor corroboration can distinguish an isolated corrupted measurement from a physical disturbance, but coordinated agents can falsely corroborate one another. This paper proposes DynaTrust-VVC, a cyber-resilient multi-agent Volt--VAR framework that assigns each incoming neighbor message a directional trust value based on the consistency between its reported voltage increment and a sensitivity-based prediction from active- and reactive-power changes. Trust falls immediately after a physics mismatch and recovers gradually after sustained consistency. DynaTrust-VVC further uses a physics-adaptive evidence threshold and counterfactual safe-voltage recovery-based mitigation. The proposed method was validated using a nonlinear IEEE 123-bus feeder with minute-resolution residential profiles and 27 matched replicates. DynaTrust-VVC identifies a coordinated three-bus attack one minute after onset in all 27 matched replicates, whereas an otherwise identical configuration with fixed neighbor trust identifies none. The composite resilience index also increases from 0.227 to 0.315, with no statistically significant increase in the benign false-alarm rate.

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arXiv2026

Large Language Model Assisted Operational Monitoring for Battery Energy Storage System Integrated Power Distribution Networks

Battery energy storage systems (BESS) are increasingly used in distribution networks for voltage regulation and demand response, which increases the volume and complexity of operational telemetry available to grid operators. This paper presents an AI-enabled monitoring framework that connects a large language model (LLM) interface with a structured telemetry database for BESS-integrated distribution system analysis. Operator questions are submitted in natural language and translated into validated SQL queries using predefined database schema information and approved KPI views. Retrieved measurements, including bus voltages, state of charge, active power, and reactive power, are evaluated against engineering constraints for voltage limits, BESS operation, and demand response tracking. The framework is validated using hardware-in-the-loop co-simulation data from a BESS-equipped distribution feeder operating under reactive power-based voltage control and price-driven demand response. Case studies show that the framework generates valid database queries, identifies repeated voltage violations, detects reactive power overshoot, and evaluates active-power tracking performance. The results show that LLM-assisted monitoring can connect structured grid telemetry with automated engineering assessment for BESS operation analysis.

cs.AI↗

arXiv2026

Holonic Graceful Transitions Across Centralized, Decentralized, Distributed, and Local Control in DER-rich Cyber-Power Distribution System

The increasing penetration of distributed energy resources (DERs) in distribution system necessitates adaptive coordination frameworks. These frameworks must remain optimal during normal operation and resilient under cyber physical disturbances. Existing coordinated control approaches are typically deployed as static architectures with limited ability to adapt when communication degrades, local instability emerges, and operating conditions become spatially heterogeneous. This work addresses the gap by proposing a DER service-independent edge autonomous holonic adaptive coordination framework. In this framework, each DER controller executes its own control actions and transitions among centralized, distributed, decentralized, and local autonomous coordination modes without always relying on static coordination commands from the grid operator. The framework preserves coordination continuity across modes by retaining local controller states while reconfiguring only the coordination topology, information exchange pattern, and fallback action associated with the active DER service. Graceful transitions are enabled through dwell timers, rate limiting, and safety overrides to prevent dynamic instability during mode changes. Volt VAR control is used as a representative distribution automation application to validate the proposed architecture in a cyber-physical Hardware-in-the-loop (HIL) testbed. The proposed approach is evaluated under diverse cyber-physical event-based scenarios, showing region-confined adaptation under localized disturbance, reduced coordination traffic during coordination switching, and node-confined mitigation under cyber attack through edge anomaly detection and neighbor-corroborated impact estimation.

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arXiv2026

Battery health prognosis using Physics-informed neural network with Quantum Feature mapping

Accurate battery health prognosis using State of Health (SOH) estimation is essential for the reliability of multi-scale battery energy storage, yet existing methods are limited in generalizability across diverse battery chemistries and operating conditions. The inability of standard neural networks to capture the complex, high-dimensional physics of battery degradation is a major contributor to these limitations. To address this, a physics-informed neural network with the Quantum Feature Mapping(QFM) technique (QPINN) is proposed. QPINN projects raw battery sensor data into a high-dimensional Hilbert space, creating a highly expressive feature set that effectively captures subtle, non-linear degradation patterns using Nyström method. These quantum-enhanced features are then processed by a physics-informed network that enforces physical constraints. The proposed method achieves an average SOH estimation accuracy of 99.46\% across different datasets, substantially outperforming state-of-the-art baselines, with reductions in MAPE and RMSE of up to 65\% and 62\%, respectively. This method was validated on a large-scale, multi-chemistry dataset of 310,705 samples from 387 cells, and further showed notable adaptability in cross-validation settings, successfully transferring from one chemistry to another without relying on target-domain SOH labels.

cs.LG↗

arXiv2024

Survey of Moving Target Defense in Power Grids: Design Principles, Tradeoffs, and Future Directions

Moving target defense (MTD) in power grids is an emerging defense technique that has gained prominence in the recent past. It aims to solve the long-standing problem of securing the power grid against stealthy attacks. The key idea behind MTD is to introduce periodic/event-triggered controlled changes to the power grid's SCADA network/physical plant, thereby invalidating the knowledge attackers use for crafting stealthy attacks. In this paper, we provide a comprehensive overview of this topic and classify the different ways in which MTD is implemented in power grids. We further introduce the guiding principles behind the design of MTD, key performance metrics, and the associated trade-offs in MTD and identify the future development of MTD for power grid security.

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arXiv2023

Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward

Advancements in digital automation for smart grids have led to the installation of measurement devices like phasor measurement units (PMUs), micro-PMUs ($μ$-PMUs), and smart meters. However, a large amount of data collected by these devices brings several challenges as control room operators need to use this data with models to make confident decisions for reliable and resilient operation of the cyber-power systems. Machine-learning (ML) based tools can provide a reliable interpretation of the deluge of data obtained from the field. For the decision-makers to ensure reliable network operation under all operating conditions, these tools need to identify solutions that are feasible and satisfy the system constraints, while being efficient, trustworthy, and interpretable. This resulted in the increasing popularity of physics-informed machine learning (PIML) approaches, as these methods overcome challenges that model-based or data-driven ML methods face in silos. This work aims at the following: a) review existing strategies and techniques for incorporating underlying physical principles of the power grid into different types of ML approaches (supervised/semi-supervised learning, unsupervised learning, and reinforcement learning (RL)); b) explore the existing works on PIML methods for anomaly detection, classification, localization, and mitigation in power transmission and distribution systems, c) discuss improvements in existing methods through consideration of potential challenges while also addressing the limitations to make them suitable for real-world applications.

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arXiv2023

Securely implementing and managing neighborhood solar with storage and peer to peer transactive energy

In this paper, we aim to leverage peer to peer (P2P) transactive energy framework for optimal control of rooftop or neighborhood solar power with battery electric storage systems (BESS). Here we propose that the multiple neighboring customers would interconnect to form a community DC grid while still being connected to the main utility grid. The proposed infrastructure would (i) increase the resilience of the customers in case the utility grid becomes unavailable, (ii) reduce AC/DC conversion losses since a majority of the DER generation is in DC, and (iii) with the help of BESS, the customers will have a lesser impact on the utility grid in terms of California 'duck curve.' The exchange of resources and further utilization of economies of scale would significantly reduce the cost of this new infrastructure. Customers will be able to fall back on the utility AC grid in case of a cyber-attack on the community DC grid, improving cyber-security further. The proposed P2P transactive energy scheme is based on cooperation among the homeowners, using additional metering nodes to allow the sale or purchase of energy among residents. While it is expected that the scope will be limited to customers within a neighborhood, multiple neighborhoods can still interact via a utility AC grid. While it is likely that the P2P controller carrying out optimal control would work seamlessly, customers' interactions will be based on a zero-trust approach. As a part of this approach, multifactor authentication, role-based access control, etc., would be utilized, significantly improving overall cybersecurity. We examine these three strategies as case studies: (i) single home with solar power, (ii) single home with Solar power and BESS, and (iii) two or more homes with solar power and community energy storage to facilitate the policymakers making the recommendation.

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arXiv2021

Reinforcement Learning based Proactive Control for Transmission Grid Resilience to Wildfire

Power grid operation subject to an extreme event requires decision-making by human operators under stressful condition with high cognitive load. Decision support under adverse dynamic events, specially if forecasted, can be supplemented by intelligent proactive control. Power system operation during wildfires require resiliency-driven proactive control for load shedding, line switching and resource allocation considering the dynamics of the wildfire and failure propagation. However, possible number of line- and load-switching in a large system during an event make traditional prediction-driven and stochastic approaches computationally intractable, leading operators to often use greedy algorithms. We model and solve the proactive control problem as a Markov decision process and introduce an integrated testbed for spatio-temporal wildfire propagation and proactive power-system operation. We transform the enormous wildfire-propagation observation space and utilize it as part of a heuristic for proactive de-energization of transmission assets. We integrate this heuristic with a reinforcement-learning based proactive policy for controlling the generating assets. Our approach allows this controller to provide setpoints for a part of the generation fleet, while a myopic operator can determine the setpoints for the remaining set, which results in a symbiotic action. We evaluate our approach utilizing the IEEE 24-node system mapped on a hypothetical terrain. Our results show that the proposed approach can help the operator to reduce load loss during an extreme event, reduce power flow through lines that are to be de-energized, and reduce the likelihood of infeasible power-flow solutions, which would indicate violation of short-term thermal limits of transmission lines.

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arXiv2020

Hybrid Voltage Stability and Security Assessment using Synchrophasors with Consideration of Generator Q-limits

Increased power demands, push for economics and limited investment in grid infrastructure have led utilities to operatepower systems closer to their stability limits. Voltage instability may trigger cascade tripping, wide-area voltage collapse and powerblackouts. Real-time voltage stability monitoring possible with deployment of phasor measurement units (PMUs) is essential totake proactive control actions and minimize the impact on system. This paper presents a novel online algorithm for a) hybridperturbation analysis-based voltage stability monitoring (HPVSM), b) including Q-limit in voltage stability index and c) real timesecurity analysis using voltage stability index. HPVSM based voltage stability index is computed using the data obtained fromlinear state estimator and PMU measurements. Typically measurement-based schemes ignore the impact of generator Q-limitsand security analysis is not feasible. The proposed HPVSM based index considers the impact of generator Q-limits violationsby anticipating the critical generators using real-time PMU measurements. Contingencies are ranked using the proposed voltagestability index for security analysis. Results simulated for the 9-bus WECC, IEEE 14, 57 and 118 bus systems highlights thesuperiority of the proposed method in real time voltage stability and security analysis

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arXiv2019

Cyber-Physical Simulation Platform for Security Assessment of Transactive Energy Systems

Transactive energy systems (TES) are emerging as a transformative solution for the problems that distribution system operators face due to an increase in the use of distributed energy resources and rapid growth in scalability of managing active distribution system (ADS). On the one hand, these changes pose a decentralized power system control problem, requiring strategic control to maintain reliability and resiliency for the community and for the utility. On the other hand, they require robust financial markets while allowing participation from diverse prosumers. To support the computing and flexibility requirements of TES while preserving privacy and security, distributed software platforms are required. In this paper, we enable the study and analysis of security concerns by developing Transactive Energy Security Simulation Testbed (TESST), a TES testbed for simulating various cyber attacks. In this work, the testbed is used for TES simulation with centralized clearing market, highlighting weaknesses in a centralized system. Additionally, we present a blockchain enabled decentralized market solution supported by distributed computing for TES, which on one hand can alleviate some of the problems that we identify, but on the other hand, may introduce newer issues. Future study of these differing paradigms is necessary and will continue as we develop our security simulation testbed.

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