Research On Injection Attack Detection And Suppression Strategy For Distributed Charging System Of Energy Storage Tram

Energy storage trams use supercapacitors as the only power source,and they have attracted widespread attention owing to their environmental protection and high efficiency.Due to the short charging time of the trams,it is necessary to use a distributed charging system to provide highpower current for trams to achieve fast charging.The cooperative charging strategy is a classic solution to the problem of charger current imbalance,which uses the shared information of the chargers.Due to the sharing of information,false data injection attacks have become a major problem that threatens the information security of the charging system,which greatly affects the reliability and service life of the charging system.In order to improve the protection capability of the charging system against false data injection attacks,two key issues of false data injection attack detection and suppression are investigated in this thesis.First,in order to solve the detection problem of false data injection attacks,an attack detection strategy based on time-frequency domain joint features is proposed.According to the impact of attack injection on the current of the charging system,the mean and standard deviation of current tracking error are extracted to construct time-domain features.The frequency-domain feature is constructed using the mean and standard deviation of the amplitude intensity error of the current spectrum.Then,the time-domain feature and frequency-domain feature are used to construct the time-frequency domain joint feature with the weighted fusion method,which is used to set the current detection threshold and determine whether there is an abnormality in the current of the charger.Thereafter,the time distribution of the time-frequency domain joint features of different chargers under attack is utilized to achieve accurate detection of false data injection attacks.Secondly,in order to suppress the impact of false data injection attacks,an attack suppression strategy based on unknown input estimation is proposed.The characteristic that the attack data is strictly superimposed on the current is used to build the charging system model with unknown input terms.According to the separable characteristics of attack data,the unknown input estimation algorithm is used to achieve the accurate estimation of attack data.Since the unknown input estimation algorithm relies on the accurate model parameters,a dynamic identification strategy is designed to obtain accurate parameters of the charging system.Then,the data compensation method is used to clean the received data and restore the charger’s current,so as to ensure that the current of the charger converges to the reference value and suppress false data injection attacks.Finally,MATLAB simulation tool is used to verify the feasibility of the proposed attack detection and suppression strategy,and a distributed charging system experimental platform is built to verify the effectiveness of the proposed false data injection attack detection and suppression strategy.