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

Md Fazley Rafy; Kamrul Hasan; Anurag K. Srivastava

eess.SYAug 30, 2026

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

Authors: Md Fazley Rafy, Kamrul Hasan, Anurag K. Srivastava

Organizations: Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26505, USA

Abstract

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.

Figures & tables

Fig. 1: Proposed DynaTrust-VVC framework for the multi-agent control pipeline, highlighting the dynamic physics-trust additions.

Symbol	Meaning	Value	Src.
AE	autoencoder $4{-}8{-}3{-}8{-}4$ , ReLU	135 param.	D
$\theta_{i}^{0}$	AE reconstruction threshold	99th pct.	V
$\eta_{i}$	safe-state discrepancy scale	95th pct.	V
$\|\mathcal{N}_{i}\|$	neighbors (strongest sensitivity)	3	D
$S^{P},S^{Q}$	voltage sensitivities	OpenDSS FD	P
$\sigma_{\mathrm{phy}}$	physics-residual scale ( 18 )	0.020 p.u.	D

TABLE I: Parameters and provenance. V = selected on validation days, P = physics-derived, D = fixed design constant.

Class	Scenario
Cyber	Bias, ramp, and replay at bus 114
Cyber	Corridor bias: $-0.035$ p.u. at 101, 114, and 450
Benign	Coupled PV drop

TABLE II: Primary evaluation scenarios (minutes 360–600).

	$D$		$M_{\mathrm{raw}}$		CRVI
Scenario	SA	DT	SA	DT	SA	DT
Bias 114	0.936	0.936	$+0.18$	$+0.26$	0.566	0.621
Ramp 114	0.001	0.000	$-0.05$	$-0.03$	0.017	0.013
Replay 114	0.000	0.000	$-0.10$	$-0.24$	0.002	0.002
Coordinated corridor	0.000	0.936	$+0.02$	$+0.03$	0.007	0.290
Benign PV-drop FAR: SA $0.115\pm 0.144$ ; DT $0.107\pm 0.099$

TABLE III: Comparison of the static-trust ablation (SA) and the proposed DynaTrust-VVC framework (DT) over 27 matched replicates.

Method	$D$	$L$	$M$	$A$	CRVI	FAR
No defense	0.000	0.500	0.000	0.996	0.000	0.000
SA	0.234	0.553	0.058	0.994	0.227	0.115
SA + PT	0.468	0.557	0.077	0.994	0.315	0.111
SA + PT + AT	0.468	0.558	0.076	0.994	0.315	0.120
DT (full)	0.468	0.561	0.076	0.994	0.315	0.107

TABLE IV: Module ablation over the four benchmark attacks

Fig. 2: Coordinated-corridor attack response at bus 101 detailing (a) voltage trajectories, (b) trust collapse preventing false physical corroboration, and (c) successful cyber score detection.

Power Systems

Power systems research studies the planning, operation, control, and protection of electrical grids and their integration with energy resources.

See all 228 papers

Game-Theoretic Reinforcement Learning based Topology-Agnostic Defense for Virtual Power Plants
Oct 10, 2026Pooja Algikar, Stefano Fenu, Kumarsinh Jhala +1Game-Theoretic RL Cybersecurity
the energy systems and infrastructure assessment division at Argonne National Laboratory
Resilient Output Containment under Undisclosed Leader Dynamics and Actuator Attacks
Jun 25, 2026Mohammadreza Nematollahi, Khashayar Khorasani, Nader MeskinCyber-Physical Systems Multi-Agent Control
Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada. · Department of Electrical Engineering, Qatar University, Doha, Qatar.
Safe Decentralized Operation of EV Virtual Power Plant with Limited Network Visibility via Multi-Agent Reinforcement Learning
Mar 24, 2026Chenghao Huang, Jiarong Fan, Weiqing Wang +1Power Systems Decentralized MARL
Department of Data Science and AI, Faculty of IT, Monash University, Australia · Monash Energy Institute, Monash University, Australia

Oct 10, 2026J/K move · Enter open · S save

Game-Theoretic Reinforcement Learning based Topology-Agnostic Defense for Virtual Power Plants

Pooja Algikar, Stefano Fenu, Kumarsinh Jhala, Siby Plathottam

the energy systems and infrastructure assessment division at Argonne National Laboratory

Game-Theoretic RL Cybersecurity CPS Security Autonomous Cyber Defense Cyber-Physical Systems Power Systems+1

Open paper AskPDF arXiv

The rapid proliferation of distributed energy resources (DERs) aggregated by virtual power plants (VPPs) elevates cybersecurity risk by expanding the attack surface. A coordinated cyberattack has the potential to disrupt distribution-system, bulk-system, and market operations. Existing cyberphysical defense strategies largely assume access to grid network models and operating conditions, which may not be available to third-party aggregators. This paper proposes a novel hybrid, perturbation-based cyber-physical defense for VPP-operated DERs that blends game-theoretic planning with model-free reinforcement learning (RL) under a Bayesian belief-update framework. Topology agnosticism is defined from the VPP information and implementation perspective: the VPP does not require access to the feeder topology, line parameters, switching configuration, or full system state. This work advances the state of the art through three contributions: (1) a multi-output Gaussian process with Hamiltonian Monte Carlo for discrete attacker-type inference, probability of detection, and confidence of detection for modeling the belief distribution; (2) moment-matched Gaussian propagation of posterior beliefs across episodes; and (3) an RL policy that adaptively integrates Stackelberg leader (VPP) actions with exploration. Furthermore, we establish equilibrium existence and derive an episode-level closed-loop utility bound connecting attacker-belief uncertainty with GT-RL action alignment. Evaluations on IEEE 13- and 123-Bus feeders with DER portfolios demonstrate effective detection under attacker-model uncertainty and evaluated feeder re-configuration conditions, while requiring no explicit feeder-topology knowledge. Overall, the results indicate that combining bi-level game-theoretic planning, nonparametric Bayesian inference, and adaptive RL is a promising direction for VPP cybersecurity.

Abstract

Power Systems

Explore similar work

Game-Theoretic Reinforcement Learning based Topology-Agnostic Defense for Virtual Power Plants

Resilient Output Containment under Undisclosed Leader Dynamics and Actuator Attacks

Safe Decentralized Operation of EV Virtual Power Plant with Limited Network Visibility via Multi-Agent Reinforcement Learning