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基于小波包变换与深度学习的超短期光伏功率预测

2011 Chinese control and Decision Conference（CCDC）

作者： Miaomiao Ma and liu xiangjie are with the school of control and computer engineering,North China Electric Power University,No.2 Beinong Road,Huilongguan,Changping District,Beijing 102206,P.R.China.Chen Hong is with the Department of control Science and engineering,Jilin University,Campus NanLing,Renmin Str.5988,130025 Changchun,PR China.

This paper considers the dynamic stabilization and tracking control of wheeled mobile robots （WMR） in the environments without *** reference to the dynamic model of WMR with nonholonomic constraints,a method based on the unifying framework of dynamic feedback linearization with dynamic compensator is presented for solving trajectory tracking as well as posture stabilization *** design procedure is discussed in the basis of dynamics model of *** and simulation results are provided to show the effectiveness of the designed controller.

关键词： Dynamic feedback linearization wheeled mobile robots nonholonomic systems tracking stabilization

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太阳能学报 2024年第5期45卷 537-546页

作者：刘源延孔小兵马乐乐刘向杰华北电力大学控制与计算机工程学院北京102206

针对光伏功率序列的复杂多变特征,提出一种基于小波包变换(WPT)的门控循环单元(GRU)光伏功率组合预测方法。首先通过相关性分析挑选重要气象因子,并利用WPT将原始光伏功率序列分解为一组子序列;然后,提出一种基于莱维飞行天牛须搜索算法... 详细信息

针对光伏功率序列的复杂多变特征,提出一种基于小波包变换(WPT)的门控循环单元(GRU)光伏功率组合预测方法。首先通过相关性分析挑选重要气象因子,并利用WPT将原始光伏功率序列分解为一组子序列;然后,提出一种基于莱维飞行天牛须搜索算法(LFBAS)的相似日选择方法,以选择相似于预测日的历史日作为输入数据集;最后,建立一组基于GRU网络的深度学习光伏功率预测模型,将每个子序列预测结果叠加得到光伏功率最终预测结果。仿真结果表明,该文所提出的预测方法在预测精度和计算效率方面具有显著优势。

关键词：光伏发电功率预测小波包变换相似日门控循环单元天牛须搜索算法

基于最大重叠离散小波变换和深度学习的光伏功率预测

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太阳能学报 2024年第5期45卷 576-583页

作者：马乐乐孔小兵郭磊刘源延刘向杰华北电力大学控制与计算机工程学院北京102206

针对光伏功率时间序列的非平稳特性,提出一种基于最大重叠离散小波变换(MODWT)和长短期记忆网络(LSTM)的光伏功率组合预测模型。利用皮尔逊相关系数确定影响光伏功率的重要气象因素,基于MODWT算法对历史光伏功率序列进行分解,将选取的... 详细信息

针对光伏功率时间序列的非平稳特性,提出一种基于最大重叠离散小波变换(MODWT)和长短期记忆网络(LSTM)的光伏功率组合预测模型。利用皮尔逊相关系数确定影响光伏功率的重要气象因素,基于MODWT算法对历史光伏功率序列进行分解,将选取的气象因素与分解得到的平稳子序列共同构成各个LSTM网络输入,通过汇总重构每个LSTM网络的子序列预测结果得到最终的光伏功率预测结果。从理论层面分析所建立的MODWT算法的完全重构性,并基于李雅普诺夫稳定性定理推导保证预测网络收敛的学习率范围。仿真对比结果显示,所提出的光伏功率预测模型在预测精度和鲁棒性方面具有明显优势。

关键词：光伏功率预测长短期记忆网络非平稳时间序列分解预测网络收敛性

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改进的自适应小波包风电波动平滑控制策略

太阳能学报 2024年第10期45卷 566-573页

作者：张晓东张怡刘向杰丁一华北理工大学电气工程学院华北电力大学新能源电力系统国家重点实验室华北电力大学控制与计算机工程学院

针对传统自适应小波包风电功率波动平滑技术存在过度平滑以及储能系统负担加重等问题，提出一种改进的自适应小波包风电功率平滑控制策略。首先，根据不同时间段风电功率波动特性，合理规划滤波时间尺度，并确定满足系统控制要求的最小... 详细信息

针对传统自适应小波包风电功率波动平滑技术存在过度平滑以及储能系统负担加重等问题，提出一种改进的自适应小波包风电功率平滑控制策略。首先，根据不同时间段风电功率波动特性，合理规划滤波时间尺度，并确定满足系统控制要求的最小滤波尺度。其次，利用自适应小波包分解滤波尺度内的风电功率，获得期望并网功率与波动功率。之后，采用由双储能电池与超级电容器组成的混合储能系统对波动功率进行平抑。最后，分析改进的平滑策略在降低储能系统负担方面的优势，并对储能系统的运行寿命进行评估。结果表明，在保证风电功率波动有效平滑的前提下，该策略既能降低储能系统的负担，又能延长其运行寿命。

关键词：风电功率储能系统电池寿命平滑功率波动自适应小波包分解双储能电池

Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge Computing for Makespan Optimization

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Tsinghua Science and Technology 2024年第3期29卷 806-817页

作者： Lei Zeng Qi liu Shigen Shen Xiaodong liu school of computer Science Nanjing University of Information Science and TechnologyNanjing 210044China school of Software Nanjing University of Information Science and TechnologyNanjing 210044China school of Information engineering Huzhou UniversityHuzhou 313000China school of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the *** management of experience data are also modified to fully utilize its value and improve learning *** learning agents usually learn from an ignorant state,which is *** such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task *** optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition *** proposed algorithm is compared with six other methods in simulation *** show that the proposed algorithm outperforms other benchmark methods regarding makespan.

关键词： edge computing task scheduling reinforcement learning makespan Double Deep Q Network(DQN)

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考虑新能源消纳的电动汽车有序充电控制策略

太阳能学报 2024年第8期45卷 94-103页

作者：马苗苗任智伟刘立成刘向杰华北电力大学控制与计算机工程学院北京102206 北京市电力公司北京100176

针对电动汽车集群入网进行无序充电会加剧峰谷差进而造成“峰上加峰”的问题,提出一种考虑新能源消纳的电动汽车分层式有序充电控制策略。首先,基于长短时间记忆神经网络理论,建立电动汽车充电负荷预测模型;其次,建立风光发电系统的数... 详细信息

针对电动汽车集群入网进行无序充电会加剧峰谷差进而造成“峰上加峰”的问题,提出一种考虑新能源消纳的电动汽车分层式有序充电控制策略。首先,基于长短时间记忆神经网络理论,建立电动汽车充电负荷预测模型;其次,建立风光发电系统的数学模型;最后,提出分层式有序充电控制策略,上层主控制系统制定电动汽车充电功率指导曲线,下层次级控制系统合理安排电动汽车充电计划,利用概率转移矩阵算法获得最佳充电控制策略。仿真结果表明,所提出的有序充电控制策略能够有效降低电网负荷峰谷差。

关键词：电动汽车神经网络新能源有序充电概率转移矩阵

Identification of the Worst-case Static Voltage Stability Margin Interval of AC/DC Power System Considering Uncertainty of Renewables

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 974-987页

作者： Wanbin liu Shunjiang Lin Yuerong Yang Mingbo liu Qifeng Li school of Electric Power engineering South China University of TechnologyGuangzhou 510640China Guangdong Key Laboratory of Clean Energy Technology South China University of TechnologyGuangzhou 511458China Department of Electrical and computer engineering University of Central FloridaOrlandoFL 32816USA CSEE IEEE

Calculation of static voltage stability margin(SVSM)of AC/DC power systems with lots of renewable energy sources(RESs)integration requires consideration of uncertain load growth and renewable energy generation *** paper presents a bi-level optimal power flow(BLOPF)model to identify the worst-case SVSM of an AC/DC power system with line commutation converter-based HVDC and multi-terminal voltage sourced converter-based HVDC transmission *** of uncertain load growth’s hypercone model and control mode switching of DC converter stations are considered in the BLOPF ***,uncertain RES output fluctuations are described as intervals,and two three-level optimal power flow(TLOPF)models are established to identify interval bounds of the system worst-case *** two TLOPF models are both transformed into max–min bi-level optimization models according to independent characteristics of different uncertain ***,transforming the inner level model into its dual form,max–min BLOPF models are simplified to single-level optimization models for direct *** results on the modified IEEE-39 bus AC/DC case and an actual large-scale AC/DC case in China indicate correctness and efficiency of the proposed identification method.

关键词： AC/DC power system control mode switching convex relaxation dual programming multi-level optimization optimal power flow static voltage stability margin

A calibration and compensation method for an industrial robot with high accuracy harmonic reducers

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Science China(Technological Sciences) 2024年第3期67卷 725-739页

作者： ZHAO YiYang liu Xun CAO JiaBin liu JinYu XU Yan YAN WeiXin school of Mechanical engineering Shanghai Jiao Tong UniversityShanghai 200240China College of computer and control engineering Northeast Forestry UniversityHarbin 150040China

Industrial serial robots need high stiffness to keep absolute pose accuracy and meet the requirements in practical applications. However, the weak stiffness feature of robot joints and the payloads affected on robot end-effector, which will also increase the pose error of robot. Especially, the existing calibration methods often consider under no-payload condition without discussing the payload state. In this paper, we report a new industrial serial robot composed by a new harmonic reducer: Model-Y, based on high accuracy and high stiffness, and a kinematic parameter calibration algorithm which is based on a harmonic reducer forcedeformation model. To decrease the accuracy effects of payload, an iterative calibration method for kinematic parameters with payload situation was proposed. Simulation and experiments are conducted to verify the effectiveness of the proposed calibration method using the self-developed industrial serial robot. The results show a remarkably improved accuracy in absolute position and orientation with the robot's payload range. The position mean error has 70% decreased to 0.1 mm and the orientation mean error diminished to less than 0.01° after calibration with compensation. Additionally, online linear and circular tests are carried out to evaluate the position error of the robot during large-scale spatial and low-speed continuous movement. The accuracy is consistent with the previous calibration results, indicating the effectiveness and advantages of the proposed strategy in this article.

关键词： industrial serial robot Model-Y harmonic calibration stiffness model online compensation

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基于主从博弈的微电网分布式能量管理策略

系统仿真学报 2024年第4期36卷 834-843页

作者：马苗苗王浩董利鹏刘向杰华北电力大学控制与计算机工程学院北京102206 中国能源建设集团广西电力设计研究院有限公司广西南宁530007

针对电力市场运营模式下,新能源微电网的整体优化运行与多方投资者的最大利益化之间的冲突问题,提出了基于双层主从博弈的微电网分布式能量管理策略。为充分考虑能源供需两侧的平衡,引入了电力交易代理商的概念,在用户侧提出基于消费者... 详细信息

针对电力市场运营模式下,新能源微电网的整体优化运行与多方投资者的最大利益化之间的冲突问题,提出了基于双层主从博弈的微电网分布式能量管理策略。为充分考虑能源供需两侧的平衡,引入了电力交易代理商的概念,在用户侧提出基于消费者满意度和可调节负荷的综合需求响应策略;考虑源-荷双方的主动性和决策能力,建立了以电力交易代理商为领导者,分布式电源代理商和负荷代理商为追随者的交易决策博弈模型,证明了stackelberg均衡解存在唯一性,采用结合二次规划的差分进化算法对所提模型进行求解。通过对包含分布式电源和社区用户的案例进行分析,验证了所提模型和策略具有良好的收敛性和经济性。

关键词：能量管理主从博弈微电网电价策略 Stackelberg均衡