Open Access

ARTICLE

Hybrid Memory-Enhanced Autoencoder with Adversarial Training for Anomaly Detection in Virtual Power Plants

Yuqiao Liu¹, Chen Pan¹, YeonJae Oh^2,*, Chang Gyoon Lim^1,*

1 Department of Computer Engineering, Chonnam National University, Yeosu, 59626, Republic of Korea
2 Department of Cultural Contents, Chonnam National University, Yeosu, 59626, Republic of Korea

* Corresponding Authors: YeonJae Oh. Email: ; Chang Gyoon Lim. Email:

Computers, Materials & Continua 2025, 82(3), 4593-4629. https://doi.org/10.32604/cmc.2025.061196

Received 19 November 2024; Accepted 15 January 2025; Issue published 06 March 2025

Abstract

Virtual Power Plants (VPPs) are integral to modern energy systems, providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data. Traditional anomaly detection methodologies often need to adequately handle these fluctuations from solar radiation and ambient temperature variations. We introduce the Memory-Enhanced Autoencoder with Adversarial Training (MemAAE) model to overcome these limitations, designed explicitly for robust anomaly detection in VPP environments. The MemAAE model integrates three principal components: an LSTM-based autoencoder that effectively captures temporal dynamics to distinguish between normal and anomalous behaviors, an adversarial training module that enhances system resilience across diverse operational scenarios, and a prediction module that aids the autoencoder during the reconstruction process, thereby facilitating precise anomaly identification. Furthermore, MemAAE features a memory mechanism that stores critical pattern information, mitigating overfitting, alongside a dynamic threshold adjustment mechanism that adapts detection thresholds in response to evolving operational conditions. Our empirical evaluation of the MemAAE model using real-world solar power data shows that the model outperforms other comparative models on both datasets. On the Sopan-Finder dataset, MemAAE has an accuracy of 99.17% and an F1-score of 95.79%, while on the Sunalab Faro PV 2017 dataset, it has an accuracy of 97.67% and an F1-score of 93.27%. Significant performance advantages have been achieved on both datasets. These results show that MemAAE model is an effective method for real-time anomaly detection in virtual power plants (VPPs), which can enhance robustness and adaptability to inherent variables in solar power generation.

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Keywords

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