This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy sto...This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.展开更多
为实现混合动力两栖车综合效率最优,提出一种功率协调预测控制策略。该策略旨在协同优化能量管理策略与车速控制策略之间的耦合关系。针对车速预测模型失配的问题,提出利用极限学习机进行实时误差预测,并通过预测值进行预测模型校正。...为实现混合动力两栖车综合效率最优,提出一种功率协调预测控制策略。该策略旨在协同优化能量管理策略与车速控制策略之间的耦合关系。针对车速预测模型失配的问题,提出利用极限学习机进行实时误差预测,并通过预测值进行预测模型校正。设计模型预测控制器实现能量管理与车速控制的实时优化控制,并通过仿真进行验证。研究结果表明:提出的策略相较于传统的基于模型预测控制的能量管理策略能够降低等效燃油消耗、荷电状态(State of Charge,SOC)标准差、母线电压标准差和电池容量衰退,降低幅度分别为9.35%、59.63%、15.79%和45.33%;通过有无模型校正的功率协调预测控制对比,表明通过模型校正可实现等效燃油消耗、SOC标准差、母线电压标准差和电池容量衰退分别降低6.95%、25.91%、13.46%和24.07%,体现了所提出的基于极限学习机模型校正的功率协调预测控制在提升燃油经济性、维持电气系统稳定性和降低电池损耗方面的优越性。展开更多
The rapid development of renewable energy sources such as wind power has brought great challenges to the power grid. Wind power penetration can be improved by using hybrid energy storage(ES) to mitigate wind power flu...The rapid development of renewable energy sources such as wind power has brought great challenges to the power grid. Wind power penetration can be improved by using hybrid energy storage(ES) to mitigate wind power fluctuation. We studied the strategy of smoothing wind power fluctuation and the strategy of hybrid ES power distribution. Firstly, an effective control strategy can be extracted by comparing constant-time low-pass filtering(CLF), variable-time low-pass filtering(VLF), wavelet packet decomposition(WPD), empirical mode decomposition(EMD) and model predictive control algorithms with fluctuation rate constraints of the identical grid-connected wind power. Moreover, the mean frequency of ES as the cutoff frequency can be acquired by the Hilbert Huang transform(HHT), and the time constant of filtering algorithm can be obtained. Then, an improved low-pass filtering algorithm(ILFA) is proposed to achieve the power allocation between lithium battery(LB) and supercapacitor(SC), which can overcome the over-charge and over-discharge of ES in the traditional low-pass filtering algorithm(TLFA). In addition, the optimized LB and SC power are further obtained based on the SC priority control strategy combined with the fuzzy control(FC) method. Finally, simulation results show that wind power fluctuation can be effectively suppressed by LB and SC based on the proposed control strategies, which is beneficial to the development of wind and storage system.展开更多
针对模块化多电平统一电能质量调节器(modular multilevel unified power quality conditioner, MMC-UPQC)六桥臂结构下的单相桥臂故障问题,提出了一种五桥臂拓扑,这种新型拓扑可实现故障情况下的电能质量补偿。首先,对MMC-UPQC串并联...针对模块化多电平统一电能质量调节器(modular multilevel unified power quality conditioner, MMC-UPQC)六桥臂结构下的单相桥臂故障问题,提出了一种五桥臂拓扑,这种新型拓扑可实现故障情况下的电能质量补偿。首先,对MMC-UPQC串并联侧的数学模型进行分析,提出了一种复合模型预测控制(hybrid model predictive control,H-MPC),所提控制方法结合了有限集模型预测控制(finite-control-set model predictive control, FCS-MPC)以及快速模型预测控制(fast model predictive control, F-MPC)。然后,通过构建两侧独立的价值函数减少了控制方法的计算量,同时也实现了五桥臂解耦控制。最后,相比传统线性(例如PI)和非线性(例如无源控制passivity-based control,PBC)的控制策略,所提复合模型预测控制在电压补偿、负序电压抑制以及谐波电流补偿等方面具有一定优势,并在一定程度上避免了复杂的参数整定及坐标变化环节。仿真实验结果证明了所提控制方法的可行性和优越性。展开更多
文摘This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.
文摘为实现混合动力两栖车综合效率最优,提出一种功率协调预测控制策略。该策略旨在协同优化能量管理策略与车速控制策略之间的耦合关系。针对车速预测模型失配的问题,提出利用极限学习机进行实时误差预测,并通过预测值进行预测模型校正。设计模型预测控制器实现能量管理与车速控制的实时优化控制,并通过仿真进行验证。研究结果表明:提出的策略相较于传统的基于模型预测控制的能量管理策略能够降低等效燃油消耗、荷电状态(State of Charge,SOC)标准差、母线电压标准差和电池容量衰退,降低幅度分别为9.35%、59.63%、15.79%和45.33%;通过有无模型校正的功率协调预测控制对比,表明通过模型校正可实现等效燃油消耗、SOC标准差、母线电压标准差和电池容量衰退分别降低6.95%、25.91%、13.46%和24.07%,体现了所提出的基于极限学习机模型校正的功率协调预测控制在提升燃油经济性、维持电气系统稳定性和降低电池损耗方面的优越性。
基金supported by National Key Research and Development Program of China (No. 2016YFB0900400)Foundation of Director of Institute of Electrical Engineering, Chinese Academy of Sciences (No. Y760141CSA)Jiangsu Province 2016 Innovation Ability Construction Special Funds (No. BM2016027)
文摘The rapid development of renewable energy sources such as wind power has brought great challenges to the power grid. Wind power penetration can be improved by using hybrid energy storage(ES) to mitigate wind power fluctuation. We studied the strategy of smoothing wind power fluctuation and the strategy of hybrid ES power distribution. Firstly, an effective control strategy can be extracted by comparing constant-time low-pass filtering(CLF), variable-time low-pass filtering(VLF), wavelet packet decomposition(WPD), empirical mode decomposition(EMD) and model predictive control algorithms with fluctuation rate constraints of the identical grid-connected wind power. Moreover, the mean frequency of ES as the cutoff frequency can be acquired by the Hilbert Huang transform(HHT), and the time constant of filtering algorithm can be obtained. Then, an improved low-pass filtering algorithm(ILFA) is proposed to achieve the power allocation between lithium battery(LB) and supercapacitor(SC), which can overcome the over-charge and over-discharge of ES in the traditional low-pass filtering algorithm(TLFA). In addition, the optimized LB and SC power are further obtained based on the SC priority control strategy combined with the fuzzy control(FC) method. Finally, simulation results show that wind power fluctuation can be effectively suppressed by LB and SC based on the proposed control strategies, which is beneficial to the development of wind and storage system.
文摘针对模块化多电平统一电能质量调节器(modular multilevel unified power quality conditioner, MMC-UPQC)六桥臂结构下的单相桥臂故障问题,提出了一种五桥臂拓扑,这种新型拓扑可实现故障情况下的电能质量补偿。首先,对MMC-UPQC串并联侧的数学模型进行分析,提出了一种复合模型预测控制(hybrid model predictive control,H-MPC),所提控制方法结合了有限集模型预测控制(finite-control-set model predictive control, FCS-MPC)以及快速模型预测控制(fast model predictive control, F-MPC)。然后,通过构建两侧独立的价值函数减少了控制方法的计算量,同时也实现了五桥臂解耦控制。最后,相比传统线性(例如PI)和非线性(例如无源控制passivity-based control,PBC)的控制策略,所提复合模型预测控制在电压补偿、负序电压抑制以及谐波电流补偿等方面具有一定优势,并在一定程度上避免了复杂的参数整定及坐标变化环节。仿真实验结果证明了所提控制方法的可行性和优越性。
