In recent years,reinforcement learning(RL)has emerged as a solution for model-free dynamic programming problem that cannot be effectively solved by traditional optimization methods.It has gradually been applied in the...In recent years,reinforcement learning(RL)has emerged as a solution for model-free dynamic programming problem that cannot be effectively solved by traditional optimization methods.It has gradually been applied in the fields such as economic dispatch of power systems due to its strong selflearning and self-optimizing capabilities.However,existing economic scheduling methods based on RL ignore security risks that the agent may bring during exploration,which poses a risk of issuing instructions that threaten the safe operation of power system.Therefore,we propose an improved proximal policy optimization algorithm for sequential security-constrained optimal power flow(SCOPF)based on expert knowledge and safety layer to determine active power dispatch strategy,voltage optimization scheme of the units,and charging/discharging dispatch of energy storage systems.The expert experience is introduced to improve the ability to enforce constraints such as power balance in training process while guiding agent to effectively improve the utilization rate of renewable energy.Additionally,to avoid line overload,we add a safety layer at the end of the policy network by introducing transmission constraints to avoid dangerous actions and tackle sequential SCOPF problem.Simulation results on an improved IEEE 118-bus system verify the effectiveness of the proposed algorithm.展开更多
城市轨道交通属于十分重要的基础设施,具有较高的安全苛求特性,其核心的列车运行控制系统信息安全是当前的研究热点。该文采用了复杂网络和强化学习等方法,研究了当前城市轨道交通中应用最为广泛的基于通信的列车运行控制(Communication...城市轨道交通属于十分重要的基础设施,具有较高的安全苛求特性,其核心的列车运行控制系统信息安全是当前的研究热点。该文采用了复杂网络和强化学习等方法,研究了当前城市轨道交通中应用最为广泛的基于通信的列车运行控制(Communication Based Train Control,CBTC)系统的信息安全风险。分析了CBTC系统的信息安全风险要素;基于复杂网络理论构建了CBTC系统的信息域模型;研究了CBTC系统的信息安全风险量化方法,并基于强化学习评估了系统最严峻的风险面。经过实例验证,提出的方法能够有效评估CBTC系统的信息安全。展开更多
An intrusion detection system(IDS)becomes an important tool for ensuring security in the network.In recent times,machine learning(ML)and deep learning(DL)models can be applied for the identification of intrusions over...An intrusion detection system(IDS)becomes an important tool for ensuring security in the network.In recent times,machine learning(ML)and deep learning(DL)models can be applied for the identification of intrusions over the network effectively.To resolve the security issues,this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique,called BBOFS-DRL for intrusion detection.The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network.To attain this,the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm(BOA)to elect feature subsets.Besides,DRL model is employed for the proper identification and classification of intrusions that exist in the network.Furthermore,beetle antenna search(BAS)technique is applied to tune the DRL parameters for enhanced intrusion detection efficiency.For ensuring the superior intrusion detection outcomes of the BBOFS-DRL model,a wide-ranging experimental analysis is performed against benchmark dataset.The simulation results reported the supremacy of the BBOFS-DRL model over its recent state of art approaches.展开更多
基金supported in part by National Natural Science Foundation of China(No.52077076)in part by the National Key R&D Plan(No.2021YFB2601502)。
文摘In recent years,reinforcement learning(RL)has emerged as a solution for model-free dynamic programming problem that cannot be effectively solved by traditional optimization methods.It has gradually been applied in the fields such as economic dispatch of power systems due to its strong selflearning and self-optimizing capabilities.However,existing economic scheduling methods based on RL ignore security risks that the agent may bring during exploration,which poses a risk of issuing instructions that threaten the safe operation of power system.Therefore,we propose an improved proximal policy optimization algorithm for sequential security-constrained optimal power flow(SCOPF)based on expert knowledge and safety layer to determine active power dispatch strategy,voltage optimization scheme of the units,and charging/discharging dispatch of energy storage systems.The expert experience is introduced to improve the ability to enforce constraints such as power balance in training process while guiding agent to effectively improve the utilization rate of renewable energy.Additionally,to avoid line overload,we add a safety layer at the end of the policy network by introducing transmission constraints to avoid dangerous actions and tackle sequential SCOPF problem.Simulation results on an improved IEEE 118-bus system verify the effectiveness of the proposed algorithm.
文摘城市轨道交通属于十分重要的基础设施,具有较高的安全苛求特性,其核心的列车运行控制系统信息安全是当前的研究热点。该文采用了复杂网络和强化学习等方法,研究了当前城市轨道交通中应用最为广泛的基于通信的列车运行控制(Communication Based Train Control,CBTC)系统的信息安全风险。分析了CBTC系统的信息安全风险要素;基于复杂网络理论构建了CBTC系统的信息域模型;研究了CBTC系统的信息安全风险量化方法,并基于强化学习评估了系统最严峻的风险面。经过实例验证,提出的方法能够有效评估CBTC系统的信息安全。
文摘An intrusion detection system(IDS)becomes an important tool for ensuring security in the network.In recent times,machine learning(ML)and deep learning(DL)models can be applied for the identification of intrusions over the network effectively.To resolve the security issues,this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique,called BBOFS-DRL for intrusion detection.The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network.To attain this,the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm(BOA)to elect feature subsets.Besides,DRL model is employed for the proper identification and classification of intrusions that exist in the network.Furthermore,beetle antenna search(BAS)technique is applied to tune the DRL parameters for enhanced intrusion detection efficiency.For ensuring the superior intrusion detection outcomes of the BBOFS-DRL model,a wide-ranging experimental analysis is performed against benchmark dataset.The simulation results reported the supremacy of the BBOFS-DRL model over its recent state of art approaches.
