Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(VPP)have become a vital new framework for energy management.LiBs are key in this context,owing to their high-efficiency energy storage capabilities essential for VPP operations.However,LiBs are prone to various abnormal states like overcharging,over-discharging,and internal short circuits,which impede power transmission efficiency.Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB data.In response,we introduce an innovative method:a Long Short-Term Memory(LSTM)autoencoder based on Dynamic Frequency Memory and Correlation Attention(DFMCA-LSTM-AE).This unsupervised,end-to-end approach is specifically designed for dynamically monitoring abnormal states in LiB data.The method starts with a Dynamic Frequency Fourier Transform module,which dynamically captures the frequency characteristics of time series data across three scales,incorporating a memory mechanism to reduce overgeneralization of abnormal frequencies.This is followed by integrating LSTM into both the encoder and decoder,enabling the model to effectively encode and decode the temporal relationships in the time series.Empirical tests on a real-world LiB dataset demonstrate that DFMCA-LSTM-AE outperforms existing models,achieving an average Area Under the Curve(AUC)of 90.73%and an F1 score of 83.83%.These results mark significant improvements over existing models,ranging from 2.4%–45.3%for AUC and 1.6%–28.9%for F1 score,showcasing the model’s enhanced accuracy and reliability in detecting abnormal states in LiB data.

Detection and localization of cyber attacks on water treatment systems:an entropy-based approach

With the advent of Industry 4.0,water treatment systems(WTSs)are recognized as typical industrial cyber-physical systems(iCPSs)that are connected to the open Internet.Advanced information technology(IT)benefits the WTS in the aspects of reliability,efficiency,and economy.However,the vulnerabilities exposed in the communication and control infrastructure on the cyber side make WTSs prone to cyber attacks.The traditional IT system oriented defense mechanisms cannot be directly applied in safety-critical WTSs because the availability and real-time requirements are of great importance.In this paper,we propose an entropy-based intrusion detection(EBID)method to thwart cyber attacks against widely used controllers(e.g.,programmable logic controllers)in WTSs to address this issue.Because of the varied WTS operating conditions,there is a high false-positive rate with a static threshold for detection.Therefore,we propose a dynamic threshold adjustment mechanism to improve the performance of EBID.To validate the performance of the proposed approaches,we built a high-fidelity WTS testbed with more than 50 measurement points.We conducted experiments under two attack scenarios with a total of 36attacks,showing that the proposed methods achieved a detection rate of 97.22%and a false alarm rate of 1.67%.