An Evolutionary Deep Reinforcement Learning-Based Framework for Efficient Anomaly Detection in Smart Power Distribution Grids

Mohammad Mehdi Sharifi Nevisi, Mehrdad Shoeibi, Francisco Hernando-Gallego, Diego Martín () and Sarvenaz Sadat Khatami
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Mohammad Mehdi Sharifi Nevisi: Department of Computer Science, Escuela de Ingeniería Informática de Segovia, Universidad de Valladolid, 40005 Segovia, Spain
Mehrdad Shoeibi: The WPI Business School, Worcester Polytechnic Institute, Worcester, MA 01609-2280, USA
Francisco Hernando-Gallego: Department of Computer Science, Escuela de Ingeniería Informática de Segovia, Universidad de Valladolid, 40005 Segovia, Spain
Diego Martín: Department of Computer Science, Escuela de Ingeniería Informática de Segovia, Universidad de Valladolid, 40005 Segovia, Spain
Sarvenaz Sadat Khatami: Department of Data Science Engineering, University of Houston, Houston, TX 77204, USA

Energies, 2025, vol. 18, issue 10, 1-36

Abstract: The increasing complexity of modern smart power distribution systems (SPDSs) has made anomaly detection a significant challenge, as these systems generate vast amounts of heterogeneous and time-dependent data. Conventional detection methods often struggle with adaptability, generalization, and real-time decision-making, leading to high false alarm rates and inefficient fault detection. To address these challenges, this study proposes a novel deep reinforcement learning (DRL)-based framework, integrating a convolutional neural network (CNN) for hierarchical feature extraction and a recurrent neural network (RNN) for sequential pattern recognition and time-series modeling. To enhance model performance, we introduce a novel non-dominated sorting artificial bee colony (NSABC) algorithm, which fine-tunes the hyper-parameters of the CNN-RNN structure, including weights, biases, the number of layers, and neuron configurations. This optimization ensures improved accuracy, faster convergence, and better generalization to unseen data. The proposed DRL-NSABC model is evaluated using four benchmark datasets: smart grid, advanced metering infrastructure (AMI), smart meter, and Pecan Street, widely recognized in anomaly detection research. A comparative analysis against state-of-the-art deep learning (DL) models, including RL, CNN, RNN, the generative adversarial network (GAN), the time-series transformer (TST), and bidirectional encoder representations from transformers (BERT), demonstrates the superiority of the proposed DRL-NSABC. The proposed DRL-NSABC model achieved high accuracy across all benchmark datasets, including 95.83% on the smart grid dataset, 96.19% on AMI, 96.61% on the smart meter, and 96.45% on Pecan Street. Statistical t -tests confirm the superiority of DRL-NSABC over other algorithms, while achieving a variance of 0.00014. Moreover, DRL-NSABC demonstrates the fastest convergence, reaching near-optimal accuracy within the first 100 epochs. By significantly reducing false positives and ensuring rapid anomaly detection with low computational overhead, the proposed DRL-NSABC framework enables efficient real-world deployment in smart power distribution systems without major infrastructure upgrades and promotes cost-effective, resilient power grid operations.

Keywords: anomaly detection; smart power distribution grids; deep reinforcement learning; recurrent neural network; non-dominated sorting artificial bee colony (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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