为应对风能、光能等自然资源的间歇性和波动性,提高风光火储综合能源系统的新能源消纳率,维护电网安全稳定运行,建立风光火储综合能源系统调度模型,提出一种自适应随机模型预测控制(Stochastic Model Predictive Control,SMPC)调度策略...为应对风能、光能等自然资源的间歇性和波动性,提高风光火储综合能源系统的新能源消纳率,维护电网安全稳定运行,建立风光火储综合能源系统调度模型,提出一种自适应随机模型预测控制(Stochastic Model Predictive Control,SMPC)调度策略。该策略主要由供需预测模型、场景削减、滚动优化及反馈校正4部分组成,通过合理削减初始场景及实时改变滚动优化周期步长等手段提升调度精确性。同时,以某典型日为算例,对比分析自适应SMPC、模型预测控制(Model Predictive Control,MPC)、SMPC三种调度策略的优劣性。结果表明,本文所提自适应SMPC调度策略可有效应对风光出力与负荷的波动性和不确定性,该策略下,调度精度较高、速度较快,且总调度成本变动较小。展开更多
The rapid adoption of machine learning in sensitive domains,such as healthcare,finance,and government services,has heightened the need for robust,privacy-preserving techniques.Traditional machine learning approaches l...The rapid adoption of machine learning in sensitive domains,such as healthcare,finance,and government services,has heightened the need for robust,privacy-preserving techniques.Traditional machine learning approaches lack built-in privacy mechanisms,exposing sensitive data to risks,which motivates the development of Privacy-Preserving Machine Learning(PPML)methods.Despite significant advances in PPML,a comprehensive and focused exploration of Secure Multi-Party Computing(SMPC)within this context remains underdeveloped.This review aims to bridge this knowledge gap by systematically analyzing the role of SMPC in PPML,offering a structured overviewof current techniques,challenges,and future directions.Using a semi-systematicmapping studymethodology,this paper surveys recent literature spanning SMPC protocols,PPML frameworks,implementation approaches,threat models,and performance metrics.Emphasis is placed on identifying trends,technical limitations,and comparative strengths of leading SMPC-based methods.Our findings reveal thatwhile SMPCoffers strong cryptographic guarantees for privacy,challenges such as computational overhead,communication costs,and scalability persist.The paper also discusses critical vulnerabilities,practical deployment issues,and variations in protocol efficiency across use cases.展开更多
为了达到能量转换最优化和减小机械结构的疲劳负荷的要求,基于双馈感应发电机风能转换系统建立了数学模型,提出一种双频环滑模预测优化控制方法。该方法采用双频环多目标结构,低频环引入基于ARMA(autoregressive and moving average mod...为了达到能量转换最优化和减小机械结构的疲劳负荷的要求,基于双馈感应发电机风能转换系统建立了数学模型,提出一种双频环滑模预测优化控制方法。该方法采用双频环多目标结构,低频环引入基于ARMA(autoregressive and moving average model)模型预测后的风速低频分量,采用PI控制对应于最优叶尖速度以保证其工作点运行在最优控制特性曲线上;高频环引入风速的湍流分量,将预测控制与滑模控制相结合实现系统的动态优化。仿真结果表明:双频环滑模预测控制有效避免了不确定性对系统的影响,实现了部分负荷状态下的最优控制特性跟踪,减少了控制输入量的变化量,降低了机械疲劳,保证了系统的优化稳定运行。展开更多
In real-world scenarios,the uncertainty of measurements cannot be handled efficiently by traditional model predictive control(MPC).A stochastic MPC(SMPC)method for handling the uncertainty of states in autonomous driv...In real-world scenarios,the uncertainty of measurements cannot be handled efficiently by traditional model predictive control(MPC).A stochastic MPC(SMPC)method for handling the uncertainty of states in autonomous driving lane-keeping scenarios is presented in this paper.A probabilistic system is constructed by considering the variance of states.The probabilistic problem is then transformed into a solvable deterministic optimization problem in two steps.First,the cost function is separated into mean and variance components.The mean component is calculated online,whereas the variance component can be calculated offline.Second,Cantelli’s inequality is adopted for the deterministic reformulation of constraints.Consequently,the original probabilistic problem is transformed into a quadratic programming problem.To validate the feasibility and effectiveness of the proposed control method,we compared the SMPC controller with a traditional MPC controller in a lane-keeping scenario.The results demonstrate that the SMPC controller is more effective overall and produces smaller steady-state distance errors.展开更多
文摘The rapid adoption of machine learning in sensitive domains,such as healthcare,finance,and government services,has heightened the need for robust,privacy-preserving techniques.Traditional machine learning approaches lack built-in privacy mechanisms,exposing sensitive data to risks,which motivates the development of Privacy-Preserving Machine Learning(PPML)methods.Despite significant advances in PPML,a comprehensive and focused exploration of Secure Multi-Party Computing(SMPC)within this context remains underdeveloped.This review aims to bridge this knowledge gap by systematically analyzing the role of SMPC in PPML,offering a structured overviewof current techniques,challenges,and future directions.Using a semi-systematicmapping studymethodology,this paper surveys recent literature spanning SMPC protocols,PPML frameworks,implementation approaches,threat models,and performance metrics.Emphasis is placed on identifying trends,technical limitations,and comparative strengths of leading SMPC-based methods.Our findings reveal thatwhile SMPCoffers strong cryptographic guarantees for privacy,challenges such as computational overhead,communication costs,and scalability persist.The paper also discusses critical vulnerabilities,practical deployment issues,and variations in protocol efficiency across use cases.
文摘为了达到能量转换最优化和减小机械结构的疲劳负荷的要求,基于双馈感应发电机风能转换系统建立了数学模型,提出一种双频环滑模预测优化控制方法。该方法采用双频环多目标结构,低频环引入基于ARMA(autoregressive and moving average model)模型预测后的风速低频分量,采用PI控制对应于最优叶尖速度以保证其工作点运行在最优控制特性曲线上;高频环引入风速的湍流分量,将预测控制与滑模控制相结合实现系统的动态优化。仿真结果表明:双频环滑模预测控制有效避免了不确定性对系统的影响,实现了部分负荷状态下的最优控制特性跟踪,减少了控制输入量的变化量,降低了机械疲劳,保证了系统的优化稳定运行。
基金the Science and Technology Commission of Shanghai Municipality(No.19511103503)。
文摘In real-world scenarios,the uncertainty of measurements cannot be handled efficiently by traditional model predictive control(MPC).A stochastic MPC(SMPC)method for handling the uncertainty of states in autonomous driving lane-keeping scenarios is presented in this paper.A probabilistic system is constructed by considering the variance of states.The probabilistic problem is then transformed into a solvable deterministic optimization problem in two steps.First,the cost function is separated into mean and variance components.The mean component is calculated online,whereas the variance component can be calculated offline.Second,Cantelli’s inequality is adopted for the deterministic reformulation of constraints.Consequently,the original probabilistic problem is transformed into a quadratic programming problem.To validate the feasibility and effectiveness of the proposed control method,we compared the SMPC controller with a traditional MPC controller in a lane-keeping scenario.The results demonstrate that the SMPC controller is more effective overall and produces smaller steady-state distance errors.
