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

作　　者：Yuqiao Liu Chen Pan YeonJae Oh Chang Gyoon Lim 

机构地区：[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

出　　处：《Computers, Materials & Continua》2025年第3期4593-4629,共37页计算机、材料和连续体(英文)

基　　金：supported by“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-002);the Technology Development Program(RS-2023-00266141)funded by the Ministry of SMEs and Startups(MSS,Republic of Korea).

摘　　要：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.

关 键 词：Virtual power plants(VPPs) anomaly detection memory-enhanced autoencoder adversarial training solar power 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]