Microgrid inverter grid disconnection/connection seamless switching control method based on phase angle estimation

本发明涉及一种基于相角估算的微电网逆变器离并网无缝切换控制方法。使用储能系统作为主电源，包括储能电池、变流器、PCS模式控制器。在并网模式下使用PQ控制，孤岛模式下使用V/F控制。从并网至孤岛切换过程中电压幅值参考值由离网前电网相电压峰值给定，电压相角参考值由离网前电网角频率平均值和离网时刻的瞬时相角计算得到。从孤岛至并网切换过程中，利用基于电网电压瞬时值的相角估计算法更改逆变器V/F模式中相角参考值，快速跟踪电网相角，在PLL稳定锁相后，使用精准的电压相角代替估算值。通过叠加频率干扰量判断微网是否并网完成，更改相应控制模式，实现无缝切换

Artificial immune pattern recognition for structure damage classification

Damage detection in structures is one of the research topics that have received growing interest in research communities. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage classification problem. This paper presents an Artificial Immune Pattern Recognition (AIPR) approach for the damage classification in structures. An AIPR-based structure damage classifier has been developed, which incorporates several novel characteristics of the natural immune system. The structure damage pattern recognition is achieved through mimicking immune recognition mechanisms that possess features such as adaptation, evolution, and immune learning. The damage patterns are represented by feature vectors that are extracted from the structure's dynamic response measurements. The training process is designed based on the clonal selection principle in the immune system. The selective and adaptive features of the clonal selection algorithm allow the classifier to evolve its pattern recognition antibodies towards the goal of matching the training data. In addition, the immune learning algorithm can learn and remember different data patterns by generating a set of memory cells that contains representative feature vectors for each class (pattern). The performance of the presented structure damage classifier has been validated using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control-American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group and a three-story frame provided by Los Alamos National Laboratory. The validation results show that the AIPR-based pattern recognition is suitable for structure damage classification. The presented research establishes a fundamental basis for the application of the AIPR concepts in the structure damage classification

Wireless sensor network routing optimization method based on immune clonal selection

本发明涉及物联网技术领域，具体涉及一种基于免疫克隆选择的无线传感器网络路由优化方法。本发明包括：在first?order射频模型的基础上建立多跳通信的能量消耗模型；将源节点与所有组播节点的通信路由视为一颗组播树，在满足约束条件下，能量消耗值最小的组播树即为最优路径；将每棵组播树作为免疫系统中的一个抗体，组播树中源节点到组播节点的一条路由为抗体的一个基因；确定通信节点的最佳数量；建立组播源节点与所有组播节点之间的通达路由备选路径集。本发明根据路由优化与人工免疫系统之间的内在关联，建立路由优化问题求解与人工免疫响应五元素结构之间对应关系，提高优化速度，显著减少无线传感器网络的自愈时间

A scheduling algorithm for home energy management system in smart grid

家庭能源管理系统是智能电网在居民侧的延伸,是智能电网领域的研究热点之一。阐述了智能电网环境下家庭能源管理系统与传统家庭能源管理系统的不同和智能电网环境下家庭能源管理系统的新功能需求。提出了智能电网环境下家庭能源管理系统的结构和功能模块,接着给出了智能电网环境下家庭能源管理系统的技术体系。从检测技术、网络通信技术和优化调度算法三方面综述了该领域的研究进展,讨论了存在的技术挑战,指出了未来的研究方向

Photovoltaic generation prediction based on similar days and neural network

光伏发电系统的输出功率受到季节、太阳辐射强度、温度和湿度等气象条件影响，呈现出时变性、间歇性和随机性。文章提出了基于相似日原理和改进的BP神经网络预测方法，利用光伏电站的历史气象信息建立气象特征向量，基于曼哈顿距离寻找相似日，根据给定的不同预测日选取3个相似日的输出功率作为预测模型输入，直接预测发电站的输出功率。以某光伏电站为例进行建模预测，并通过预测误差分析证明了算法的有效性。</p

Intelligent grid disconnection/connection switch with detection function, and implementation method therefor

本发明涉及具有检测功能的智能离/并网开关及其实现方法，开关包括DSP处理器模块与电源模块、时钟模块、检测模块、继电器控制模块、通信模块、传感器模块连接；继电器控制模块与断路器连接；其方法包括以下步骤：DSP处理器模块根据所检测的AD传感器得到的电压与电流数据判断微电网侧与大电网侧的交流电压相位差；如果未连接，则继电器控制模块保持三相断路器切断状态；当DSP处理器通过通信模块得到微电网中央控制器的并网信号，且当微电网侧与大电网侧的电压相位差小于规定值&amp;时，控制继电器合闸断路器；本发明将并网点检测功能，自动控制功能，网络通信功能集成到一个开关当中，具有通用性强，功能丰富，使用方便等优点

Household User Behavior Analysis based on Power Data

With the popularization of smart grid application and the rapid development of big data technology, a large amount of user power consumption data is recorded and stored, making user power consumption behavior analysis the key content of demand side management. The user behavior state is identified by the variation of power amplitude of power load based on hidden Markov model. Users are mapped to potential feature space by means of price elasticity information regularization matrix decomposition. Further, K-means clustering algorithm is adopted to segment users. The strategy can improve the rationality of user power consumption behavior clustering, more effectively grasp the form of user power consumption load, reduce the peak load of power and balance power supply and demand, which is of great significance to the demand side management of smart grid

Genetic based reinforcement learning load control for smart grids

As the improvement of smart grids, the customer participation has reinvigorated interest in demand-side features such as load control for domestic users. A genetic based reinforcement learning (RL) load controller is proposed. The genetic is used to adjust the parameters of the controller. The RL algorithm, which is independent of the mathematic model, shows the particular superiority in load control. By means of learning procedures, the proposed controller can learn to take the best actions to regulate the energy usage for equipments with the features of high comfortable for energy usage and low electric charge meanwhile. Simulation results show that the proposed load controller can promote the performance energy usage in smart grids

Research on Simulation Platform of Wireless Sensor Network Based on Multi-Agent

超大规模的无线传感器网络系统通过个体协同表现出群体智能行为特性,其理论分析和物理实验系统构建的困难使得仿真成为目前的主要研究手段。因此,采用基于角色的面向Agent程序设计方法,从体系结构、角色类划分、角色间关系、角色绑定以及Agent类设计等方面论述了无线传感器网络仿真平台的设计与实现。该平台不仅能模拟无线传感器网络系统的功能执行,而且能体现其群体智能行为特征

Research on Simulation Platform of Wireless Sensor Network Based on Multi-Agent

超大规模的无线传感器网络系统通过个体协同表现出群体智能行为特性,其理论分析和物理实验系统构建的困难使得仿真成为目前的主要研究手段。因此,采用基于角色的面向Agent程序设计方法,从体系结构、角色类划分、角色间关系、角色绑定以及Agent类设计等方面论述了无线传感器网络仿真平台的设计与实现。该平台不仅能模拟无线传感器网络系统的功能执行,而且能体现其群体智能行为特征