针对当前智能电表现场检定效率低、人力成本高、实时性差、无法全量监测等问题,提出一种基于动态线损和渐消记忆递推最小二乘法(dynamic line loss and fading memory recursive least square,DLL-FMRLS)的智能电表误差在线估计算法。首...针对当前智能电表现场检定效率低、人力成本高、实时性差、无法全量监测等问题,提出一种基于动态线损和渐消记忆递推最小二乘法(dynamic line loss and fading memory recursive least square,DLL-FMRLS)的智能电表误差在线估计算法。首先,通过分析台区线损与供电量之间的关系,对传统模型进行改进,提出动态线损误差模型,该模型中线损可随实际供电量变化,使得模型获得的误差估计值更接近实际值;然后,利用FMRLS算法求解动态线损误差模型,以获得智能电表运行误差;最后,根据某省电网公司的实际数据对算法现场验证。结果结果表明,与列文伯格–马夸尔特(Levenberg-Marquardt, LM)算法和限定记忆最小二乘(limited memory recursive least squares,LMRLS)算法相比,所提算法可以有效提高智能电表的误差估计的准确度。展开更多
锂离子电池由于其高能量密度、高循环寿命等优点被广泛应用于电力储能和新能源汽车中。准确估计电池的荷电状态(state of charge,SOC)对提高电池使用寿命和利用效率具有重要意义。然而,锂电池是一个高度复杂、时变和非线性的电化学系统...锂离子电池由于其高能量密度、高循环寿命等优点被广泛应用于电力储能和新能源汽车中。准确估计电池的荷电状态(state of charge,SOC)对提高电池使用寿命和利用效率具有重要意义。然而,锂电池是一个高度复杂、时变和非线性的电化学系统。因此,精度高的在线SOC估计方法对锂电池的实际应用非常重要。近年来,基于模型的SOC估计方法由于其闭环控制、易于实现等特点被广泛关注和研究。本文从模型分类、模型参数辨识算法、SOC估计算法以及SOC估计影响因素对基于模型的SOC估计方法进行综述,首先归纳总结了各种常见的锂离子电池模型,主要介绍了各种常见电化学模型和等效电路模型并进行对比分析;然后重点对模型建立方法和SOC状态估计算法进行梳理和对比,主要介绍了各种模型参数辨识方法及SOC估计方法并进行了对比分析;之后对影响基于模型的SOC估计方法精度的影响因素及解决方法进行分析和总结,主要从温度、老化以及电池组对电池SOC估计的影响进行分析;最后对未来的研究方向进行了讨论和展望。展开更多
An online algorithm for training LS-SVM (Least Square Support VectorMachines) was proposed for the application of function estimation and classification. Online LS-SVMmeans that LS-SVM can be trained in an incremental...An online algorithm for training LS-SVM (Least Square Support VectorMachines) was proposed for the application of function estimation and classification. Online LS-SVMmeans that LS-SVM can be trained in an incremental way, and can be pruned to get sparseapproximation in a decremental way. When a SV (Support Vector) is added or removed, the onlinealgorithm avoids computing large-scale matrix inverse. Thus the computation cost is reduced. Onlinealgorithm is especially useful to realistic function estimation problem such as systemidentification. The experiments with benchmark function estimation problem and classificationproblem show the validity of this online algorithm.展开更多
The remaining useful life(RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety(safety awareness and safety improvement). ...The remaining useful life(RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety(safety awareness and safety improvement). These studies incorporated many di erent models, algorithms, and techniques for modeling and assessment. In this paper, methods of RUL assessment are summarized and expounded upon using two major methods: physics model based and data driven based methods. The advantages and disadvantages of each of these methods are deliberated and compared as well. Due to the intricacy of failure mechanism in system, and di culty in physics degradation observation, RUL assessment based on observations of performance variables turns into a science in evaluating the degradation. A modeling method from control systems, the state space model(SSM), as a first order hidden Markov, is presented. In the context of non-linear and non-Gaussian systems, the SSM methodology is capable of performing remaining life assessment by using Bayesian estimation(sequential Monte Carlo). Being e ective for non-linear and non-Gaussian dynamics, the methodology can perform the assessment recursively online for applications in CBM(condition based maintenance), PHM(prognostics and health management), remanufacturing, and system performance reliability. Finally, the discussion raises concerns regarding online sensing data for SSM modeling and assessment of RUL.展开更多
The orbital pursuit-evasion game is typically formulated as a complete-information game,which assumes the payoff functions of the two players are common knowledge.However,realistic pursuit-evasion games typically have...The orbital pursuit-evasion game is typically formulated as a complete-information game,which assumes the payoff functions of the two players are common knowledge.However,realistic pursuit-evasion games typically have incomplete information,in which the lack of payoff information limits the player’s ability to play optimally.To address this problem,this paper proposes a currently optimal escape strategy based on estimation for the evader.In this strategy,the currently optimal evasive controls are first derived based on the evader’s guess of the pursuer’s payoff weightings.Then an online parameter estimation method based on a modified strong tracking unscented Kalman filter is employed to modify the guess and update the strategy during the game.As the estimation becomes accurate,the currently optimal strategy gets closer to the actually optimal strategy.Simulation results show the proposed strategy can achieve optimal evasive controls progressively and the evader’s payoff of the strategy is lower than that of the zero-sum escape strategy.Meanwhile,the proposed strategy is also effective in the case where the pursuer changes its payoff function halfway during the game.展开更多
文摘针对当前智能电表现场检定效率低、人力成本高、实时性差、无法全量监测等问题,提出一种基于动态线损和渐消记忆递推最小二乘法(dynamic line loss and fading memory recursive least square,DLL-FMRLS)的智能电表误差在线估计算法。首先,通过分析台区线损与供电量之间的关系,对传统模型进行改进,提出动态线损误差模型,该模型中线损可随实际供电量变化,使得模型获得的误差估计值更接近实际值;然后,利用FMRLS算法求解动态线损误差模型,以获得智能电表运行误差;最后,根据某省电网公司的实际数据对算法现场验证。结果结果表明,与列文伯格–马夸尔特(Levenberg-Marquardt, LM)算法和限定记忆最小二乘(limited memory recursive least squares,LMRLS)算法相比,所提算法可以有效提高智能电表的误差估计的准确度。
文摘锂离子电池由于其高能量密度、高循环寿命等优点被广泛应用于电力储能和新能源汽车中。准确估计电池的荷电状态(state of charge,SOC)对提高电池使用寿命和利用效率具有重要意义。然而,锂电池是一个高度复杂、时变和非线性的电化学系统。因此,精度高的在线SOC估计方法对锂电池的实际应用非常重要。近年来,基于模型的SOC估计方法由于其闭环控制、易于实现等特点被广泛关注和研究。本文从模型分类、模型参数辨识算法、SOC估计算法以及SOC估计影响因素对基于模型的SOC估计方法进行综述,首先归纳总结了各种常见的锂离子电池模型,主要介绍了各种常见电化学模型和等效电路模型并进行对比分析;然后重点对模型建立方法和SOC状态估计算法进行梳理和对比,主要介绍了各种模型参数辨识方法及SOC估计方法并进行了对比分析;之后对影响基于模型的SOC估计方法精度的影响因素及解决方法进行分析和总结,主要从温度、老化以及电池组对电池SOC估计的影响进行分析;最后对未来的研究方向进行了讨论和展望。
基金This project was financially supported by the National Natural Science Foundation of China (No. 69889050)
文摘An online algorithm for training LS-SVM (Least Square Support VectorMachines) was proposed for the application of function estimation and classification. Online LS-SVMmeans that LS-SVM can be trained in an incremental way, and can be pruned to get sparseapproximation in a decremental way. When a SV (Support Vector) is added or removed, the onlinealgorithm avoids computing large-scale matrix inverse. Thus the computation cost is reduced. Onlinealgorithm is especially useful to realistic function estimation problem such as systemidentification. The experiments with benchmark function estimation problem and classificationproblem show the validity of this online algorithm.
基金Supported by Fundamental Research Funds for the Central Universities of China(Grant No.DUT17GF214)
文摘The remaining useful life(RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety(safety awareness and safety improvement). These studies incorporated many di erent models, algorithms, and techniques for modeling and assessment. In this paper, methods of RUL assessment are summarized and expounded upon using two major methods: physics model based and data driven based methods. The advantages and disadvantages of each of these methods are deliberated and compared as well. Due to the intricacy of failure mechanism in system, and di culty in physics degradation observation, RUL assessment based on observations of performance variables turns into a science in evaluating the degradation. A modeling method from control systems, the state space model(SSM), as a first order hidden Markov, is presented. In the context of non-linear and non-Gaussian systems, the SSM methodology is capable of performing remaining life assessment by using Bayesian estimation(sequential Monte Carlo). Being e ective for non-linear and non-Gaussian dynamics, the methodology can perform the assessment recursively online for applications in CBM(condition based maintenance), PHM(prognostics and health management), remanufacturing, and system performance reliability. Finally, the discussion raises concerns regarding online sensing data for SSM modeling and assessment of RUL.
基金the National Natural Science Foundation of China(Grant Nos.11572345&11972044)the Program of National University of Defense Technology(Grant No.ZK18-03-07)。
文摘The orbital pursuit-evasion game is typically formulated as a complete-information game,which assumes the payoff functions of the two players are common knowledge.However,realistic pursuit-evasion games typically have incomplete information,in which the lack of payoff information limits the player’s ability to play optimally.To address this problem,this paper proposes a currently optimal escape strategy based on estimation for the evader.In this strategy,the currently optimal evasive controls are first derived based on the evader’s guess of the pursuer’s payoff weightings.Then an online parameter estimation method based on a modified strong tracking unscented Kalman filter is employed to modify the guess and update the strategy during the game.As the estimation becomes accurate,the currently optimal strategy gets closer to the actually optimal strategy.Simulation results show the proposed strategy can achieve optimal evasive controls progressively and the evader’s payoff of the strategy is lower than that of the zero-sum escape strategy.Meanwhile,the proposed strategy is also effective in the case where the pursuer changes its payoff function halfway during the game.
