Topology-Aware Reinforcement Learning over Graphs for Resilient Power Distribution Networks

Roshni Anna Jacob; Prithvi Poddar; Jaidev Goel; Souma Chowdhury; Yulia R. Gel; Jie Zhang

eess.SYMar 7, 2026

Topology-Aware Reinforcement Learning over Graphs for Resilient Power Distribution Networks

Authors: Roshni Anna Jacob, Prithvi Poddar, Jaidev Goel, Souma Chowdhury, Yulia R. Gel, Jie Zhang

Organizations: The University of Texas at Dallas, Richardson, TX, USA · University at Buffalo, Buffalo, NY, USA · Virginia Tech, Blacksburg, VA, USA

Abstract

Extreme weather events and cyberattacks can cause component failures and disrupt the operation of power distribution networks (DNs), during which reconfiguration and load shedding are often adopted for resilience enhancement. This study introduces a topology-aware graph reinforcement learning (RL) framework for outage management that embeds higher-order topological features of the DN into a graph-based RL model, enabling reconfiguration and load shedding to maximize energy supply while maintaining operational stability. Results on the modified IEEE 123-bus feeder across 300 diverse outage scenarios demonstrate that incorporating the topological data analysis (TDA) tool, persistence homology (PH), yields 9-18% higher cumulative rewards, up to 6% increase in power delivery, and 6-8% fewer voltage violations compared to a baseline graph-RL model. These findings highlight the potential of integrating RL with TDA to enable self-healing in DNs, facilitating fast, adaptive, and automated restoration.

Figures & tables

Fig. 1 : Architecture of the policy network (PH-GCAPCN).

Panel of Figure 2 — Fig. 2 : Training performance for the proposed RL model based on PH-GCAPCN and the baseline RL model using GCAPCN.

Metric	Test Set 1		Test Set 2		Test Set 3
	PH-GCAPCN	GCAPCN	PH-GCAPCN	GCAPCN	PH-GCAPCN	GCAPCN
Reward ( $R$ )	0.4626 $\pm$ 0.5757	0.3923 $\pm$ 0.5692	0.5132 $\pm$ 0.5540	0.4710 $\pm$ 0.5407	0.5538 $\pm$ 0.4842	0.5010 $\pm$ 0.4659
Energy Supplied ( $E_{\text{supp}}$ )	0.6760 $\pm$ 0.3433	0.6374 $\pm$ 0.3265	0.7119 $\pm$ 0.3234	0.6821 $\pm$ 0.3129	0.7302 $\pm$ 0.2913	0.6921 $\pm$ 0.2769
Voltage Violation ( $V_{\text{viol}}$ )	0.2134 $\pm$ 0.2330	0.2282 $\pm$ 0.2276	0.1932 $\pm$ 0.2189	0.2057 $\pm$ 0.2166	0.1763 $\pm$ 0.1942	0.1911 $\pm$ 0.1909

TABLE I : Performance of PH-GCAPCN and GCAPCN RL models across three test samples. Values are reported as mean

\pm

std over 100 distinct out-of-sample scenarios.

Test Set	Reward	Energy Supplied	Voltage Violation
1	$6.83\times 10^{-4}{}^{***}$	$1.26\times 10^{-8}{}^{***}$	$9.42\times 10^{-4}{}^{***}$
2	$3.82\times 10^{-5}{}^{***}$	$3.75\times 10^{-9}{}^{***}$	$2.73\times 10^{-5}{}^{***}$
3	$2.15\times 10^{-7}{}^{***}$	$7.39\times 10^{-8}{}^{***}$	$9.04\times 10^{-5}{}^{***}$

TABLE II : Paired t-test

p

-values comparing PH-GCAPCN and GCAPCN over 100 matched outage scenarios per test sample.

Panel of Figure 5 — Fig. 3 : Test performance comparison of PH-GCAPCN and GCAPCN models

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May 26, 2026J/K move · Enter open · S save

Outage Detection in Self-Healing Smart Grids Using Reinforcement Learning with Spectral Graph Neural Networks

Lihui Liu, Mucun Sun, Caisheng Wang

Wayne State University, Detroit, MI, USA

RL Control Graph Neural Networks Spectral Graph Convolution Power Systems

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Self-healing smart grids can quickly adjust their network configuration during outages to minimize power disruptions. During an outage, several actions can be taken, such as network reconfiguration through switching operations and emergency load shedding. However, traditional machine learning methods for outage mitigation are not well suited for smart grids due to their slow response time and high computational cost. To address these challenges, recent studies have explored reinforcement learning to automatically perform network reconfiguration. In these approaches, the control policy is typically modeled using a graph neural network (GNN). However, conventional GNNs operate in the spatial domain and may fail to capture important relationships in the frequency domain. Frequency-domain information is particularly useful for modeling global structural patterns and system-wide interactions in power networks. In this paper, we propose a spectral graph reinforcement learning framework for outage management in distribution networks to enhance system resilience. Our model learns the optimal power restoration policy using a spectral graph neural network. We evaluate the proposed method on three modified IEEE test systems: the 13-bus, 34-bus, and 123-bus networks. Experimental results show that our approach achieves near-optimal performance in real time and generalizes well across a wide range of outage scenarios.

Abstract

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