电池健康状况的在线估计对于电池管理系统一直是一个非常重要的问题。近年来,由于其具有灵活性和无模型优势,基于数据驱动的方法在在线健康状态(state of health,SOH)估计领域展现出极大的潜力。文中针对现有的大部分基于数据驱动的SOH...电池健康状况的在线估计对于电池管理系统一直是一个非常重要的问题。近年来,由于其具有灵活性和无模型优势,基于数据驱动的方法在在线健康状态(state of health,SOH)估计领域展现出极大的潜力。文中针对现有的大部分基于数据驱动的SOH估计方法存在计算量大以及较难在BMS微控制器中实现等问题,提出一种采用片段充电曲线和核岭回归(kernel ridge regression,KRR)的锂离子电池SOH估计方法。KRR是一种基于核方法的非线性回归算法,通过将核技巧与岭回归结合,能够建立充电电压片段和SOH之间的非线性映射关系。在2个公开锂离子电池老化数据集上的实验表明,该方法只需采用实际电池使用工况中容易获得的充电电压片段,就能够实现快速准确的SOH估计,并且应用到现有的BMS微控制器中。展开更多
This paper proposes a reactive power and voltage optimal control method for wind farms based on data-driven power flow.Because no prior knowledge of wind farm parameters is necessary,the proposed method is model-free....This paper proposes a reactive power and voltage optimal control method for wind farms based on data-driven power flow.Because no prior knowledge of wind farm parameters is necessary,the proposed method is model-free.Based on Koopman operator-based method,this paper constructs a power flow model of wind farms connected to the grid by using state space mapping and lift-dimension linearization.Considering reactive power devices such as wind turbines and static var generator(SVG)in wind farms,a global sensitivity-based reactive power and voltage linear optimization control model is proposed.Taking minimum reactive power adjustment of wind turbines and SVG as the objective function,combined with the sensitivity relationship between node voltage and reactive power injection,the proposed model-free voltage control method can realize optimal reactive power distribution,effectively reduce active power loss,and satisfy the requirement of rapid voltage control response of wind farms.Based on historical data of a wind farm in Ningxia,feasibility of the proposed voltage optimal control method under inaccurate parameters is verified.Compared with model-based methods,the proposed method exhibits advantages on parameter dependency and efficiency.展开更多
文摘电池健康状况的在线估计对于电池管理系统一直是一个非常重要的问题。近年来,由于其具有灵活性和无模型优势,基于数据驱动的方法在在线健康状态(state of health,SOH)估计领域展现出极大的潜力。文中针对现有的大部分基于数据驱动的SOH估计方法存在计算量大以及较难在BMS微控制器中实现等问题,提出一种采用片段充电曲线和核岭回归(kernel ridge regression,KRR)的锂离子电池SOH估计方法。KRR是一种基于核方法的非线性回归算法,通过将核技巧与岭回归结合,能够建立充电电压片段和SOH之间的非线性映射关系。在2个公开锂离子电池老化数据集上的实验表明,该方法只需采用实际电池使用工况中容易获得的充电电压片段,就能够实现快速准确的SOH估计,并且应用到现有的BMS微控制器中。
基金supported by the State Key Laboratory of Power System and Generation Equipment(SKLD21KM03)National Natural Science Foundation of China(52007129).
文摘This paper proposes a reactive power and voltage optimal control method for wind farms based on data-driven power flow.Because no prior knowledge of wind farm parameters is necessary,the proposed method is model-free.Based on Koopman operator-based method,this paper constructs a power flow model of wind farms connected to the grid by using state space mapping and lift-dimension linearization.Considering reactive power devices such as wind turbines and static var generator(SVG)in wind farms,a global sensitivity-based reactive power and voltage linear optimization control model is proposed.Taking minimum reactive power adjustment of wind turbines and SVG as the objective function,combined with the sensitivity relationship between node voltage and reactive power injection,the proposed model-free voltage control method can realize optimal reactive power distribution,effectively reduce active power loss,and satisfy the requirement of rapid voltage control response of wind farms.Based on historical data of a wind farm in Ningxia,feasibility of the proposed voltage optimal control method under inaccurate parameters is verified.Compared with model-based methods,the proposed method exhibits advantages on parameter dependency and efficiency.
