面向智能电网的云数据处理系统评价方法

智能电网数据处理系统需要存储从巨量设备端点周期性采集到的海量数据,处理高度并发的读写请求,并要求系统具有良好的可扩展性.新兴的云数据服务系统为我们提供了很好的选择.但是现在云数据服务系统种类繁多,各方面的性能存在差异,如何选择合适的云数据服务系统成为关键问题.基于智能用电场景,提出了一种对云数据服务系统的性能评价和分析方法,并利用该方法评测HBase、Voldemort和Cassandra三者的性能.     

Top-K Query Framework in Wireless Sensor Networks for Smart Grid

The smart grid has caught great attentions in recent years, which is poised to transform a centralized, producer-controlled network to a decentralized, consumer- interactive network that＇s supported by fine-grained monitoring. Large-scale WSNs （Wireless Sensor Networks） have been considered one of the very promising technologies to support the implementation of smart grid. WSNs are applied in almost every aspect of smart grid, including power generation, power transmission, power distribution, power utilization and power dispatch, and the data query processing of ＇WSNs in power grid＇ become an hotspot issue due to the amount of data of power grid is very large and the requirement of response time is very high. To meet the demands, top-k query processing is a good choice, which performs the cooperative query by aggregating the database objects＇ degree of match for each different query predicate and returning the best k matching objects. In this paper, a framework that can effectively apply top-k query to wireless sensor network in smart grid is proposed, which is based on the cluster-topology sensor network. In the new method, local indices are used to optimize the necessary query routing and process intermediate results inside the cluster to cut down the data traffic, and the hierarchical join query is executed based on the local results.Besides, top-k query results are verified by the clean-up process, and two schemes are taken to deal with the problem of node＇s dynamicity, which further reduce communication cost. Case studies and experimental results show that our algorithm has outperformed the current existing one with higher quality results and better efficiently.     

Stuck-at Fault Analytics of IoT Devices Using Knowledge-based Data Processing Strategy in Smart Grid

Wireless Personal Communications - Smart grid addresses traditional electricity generation issues by integrating ambient intelligence in actions of connected devices and production processing...     

Towards Fault-Tolerant Fine-Grained Data Access Control for Smart Grid

Data access control within smart grids is a challenging issue because of the environmental noise and interferences. On one hand side, fine-grained data access control is essential because illegal access to the sensitive data may cause disastrous implications and/or be prohibited by the law. On the other hand, fault tolerance of the access control is very important, because of the potential impacts (implied by the errors) which could be significantly more serious than the ones regarding general data. In particular, control bits corruption could invalidate the security operation. To address the above challenges, this paper proposes a dedicated data access control scheme that is able to enforce fine-grained access control and resist against the corruptions implied by the noisy channels and the environmental interferences. The proposed scheme exploits a state-of-the-art cryptographic primitive called Fuzzy identity-based encryption with the lattice based access control and dedicated error-correction coding. We evaluate our proposed scheme by extensive simulations in terms of error correcting capability and energy consumption and results show the efficiency and feasibility of the proposed scheme. To our best knowledge, this paper is the first which addresses fault tolerant fine-grained data access control for smart grid.     

基于云计算的电网预测模型与自适应调度研究

光伏发电系统被视为面向偏远地区与小型孤立社区供电的优秀解决方案,但也存在电力供应不稳定的问题。针对光伏发电的能源管理问题,提出一种基于云计算的智能电网管理方案,在对电力供需两端做出预测的基础上,确定最优的电量调度策略。该方案使用小波递归神经网络模型(Wavelet Recurrent Neural Network,WRNN),使得可以高效且准确地同时对电力供需两端进行预测。此外,云计算体系使得可以在基于大量数据的电力供需预测任务上进行快速和分布式的计算能力支持。     

基于数据挖掘的电力云资源规划调度

随着虚拟化技术的应用,数据中心的资源利用率已经得到一定程度的提高,但是云资源通常还是根据用户提出的需求预先分配,资源利用率仍然有待提高.为了进一步改进资源利用率,云中心的实际资源需求可分为“周期资源”及“峰期资源”.“周期资源”可以通过历史规律,利用递阶成分负载模型进行分析,预测出数据中心大部分时间的资源需求;“峰期资源”主要是满足短时间内的各种高峰资源需求,这种需求应用了排队论及随机均衡算法模型并根据实际应用的需要动态启用、分配和收回资源.通过使用资源收集与分配守护进程对云中心资源需求进行规划调度实验,效果明显,从而为云中心资源利用率的提升及节能减排提供了一种有效途径.     

一种面向智能电网数据采集的传感器聚合布局构造算法

智能电网中分布着大量的无线传感器用于监测智能电网设备和用户的运营状态信息,原始监测数据都采集到数据处理中心会给数据采集通信网络带来极大的数据流量压力。采用在数据采集过程中进行数据聚合的策略,将极大地缩减数据流量,降低通信网络的开销。因此聚合节点的选择以及聚合拓扑的构造成为智能电网数据采集的关键问题。该文提出一种基于层次聚类的异步分布式聚合布局构造算法。该算法首先按照层次聚类把所有节点按照距离的远近聚合构造出一棵采集树。随后计算出最佳分组数,按照该分组数进行分组。然后按照异步分布式策略进行最佳聚合节点的选择以及最佳传输拓扑的构造。仿真实验表明,该算法可以快速找到具有最小开销的数据聚合方式,提高智能电网数据采集网络的效率。     

“智能电网综述”技术讲座 第一讲 智能电网的核心内涵和技术框架

本讲介绍智能电网的起源和发展动力、与传统电网的主要区别、核心内涵、技术框架及其实现顺序等问题。智能电网的提出不是偶然的,它是21世纪科学技术和经济社会发展的必然要求。智能电网的核心内涵包括自愈性、用户互动性、优质的电能质量、可再生能源发电方便的接入和分布式储能、成熟的电力市场和资产优化管理等。智能电网由高级量测体系AMI、高级配电运行ADO、高级输电运行ATO和高级资产管理AAM等4部分组成。     

智能电网中客户数据安全策略的分析

随着用电人数和用电量的快速增加,各地都开始建设智能电网,以合理调配电力资源。文中首先分析了客户数据安全保障工作在智能电网中的重要性,其次分析了智能电网中数据安全保障工作的现状,并探究了智能电网面临的网络安全威胁,最后提出了优化数据安全保障工作的策略,以期为各地的智能电网管理人员提供借鉴,推动智能电网数据保护工作的发展。     

Fog Computing for Big Data Analytics in IoT Aided Smart Grid Networks

Wireless Personal Communications - The recent integration of Internet of Things and Cloud Computing (CC) technologies into a Smart Grid (SG) revolutionizes its operation. The scalable and unlimited...     

Design of an Intelligent Self-Healing Smart Grid using a Hybrid Multi-Agent Framework

This paper discusses the applications of a hybrid multi-agent framework for self-healing applications in an intelligent smart grid system following catastrophic disturbances such as loss of generators or during system fault.The proposed hybrid multi-agent framework is a hybrid of both centralized and decentralized scheme to allow distributed intelligent agent in the smart grid system to make fast local decision while allowing the slower central controller to judge the effectiveness of the decision made by the local agents and to suggest more optimal solutions.     

Integrating Smart Building Energy Data into Smart Grid Applications in the Intelligent Secondary Substations

Automation has arrived in the low voltage grid domain. In the next few years, the secondary substation —at the barriers of medium and low voltage grids—will thus be upgraded to enable novel functions. In this paper, we present various smart grid applications running on such intelligent secondary substations (iSSN) including their interaction with each other. We integrate energy consumption and production data, as well as forecasts, sensed from the smart buildings’ energy management systems (BEMSs) into the operation of the low voltage grid. A suitable framework for those modular applications includes features to initiate their installation, update, removal, the remote operator site, and not requiring staff on-site for such typical reappearing maintenance tasks.     

基于服务的网格数据管理框架研究与应用

文章基于OGSA设计了一种网格数据管理框架．为网格环境中分布式异构数据提供方便、统一的管理和操作方式。描述了基于服务的网格数据管理框架的层次结构及功能，为网格数据管理系统的设计与实现提供了一种新的思路。     

Adaptive Processing of Digital Broadband Seismic Data

The main purpose of this paper is to develop a step-wise approach to recursive parameter estimation for time-invariant autoregressive moving average (ARMA) models used to track slowly time variant-seismic noise. In these steps computational complexity is balanced against estimation accuracy. By updating with every new data sample, the recursions are well adapted to on-line implementation. They are designed to be insensitive to spurious additive glitches in the data. Assuming that the ARMA parameters vary slowly with time, the estimated parameters contain information about the long time behavior of the modeled process compared with the time duration of additive transient signals. In seismological applications these transients are thought to be earthquake signals. The estimated ARMA parameters are used a) for the design of robust prediction error filters with arbitrary prediction distance to reduce the microseismic noise while passing the earthquake signal widely undisturbed, and b) for automatic detection of earthquake signals. A three-step scheme for the detection of weak earthquake signals is developed: The first step is to clean the data from glitches (for example data transmission errors) by replacing these with predicted values. The second step involves conventional recursive bandpass filtering to focus upon relevant frequency bands. In the third step a detection variable is computed from the difference of time consecutive ARMA parameter vectors for the bandpass filtered traces.     

支持第三方仲裁的智能电网数据安全聚合方案

智能电网作为新一代的电力系统,显著提高了电力服务的效率、可靠性和可持续性,但用户侧信息安全问题也日渐突出.本文针对智能电网系统中用户数据信息泄露的问题,提出了一个具有隐私保护的数据安全采集方案.收集器能够对采集到的电表数据进行验证,聚合为一个新的数据包,发送给电力服务中心解密和存储,且第三方仲裁机构能够解决用户端智能电表与电力服务中心发生的纠纷.同时,本方案支持收集器,电力服务中心和第三方仲裁机构执行批量验证操作,以提升验证效率.本文的理论分析与实验比较表明,该方案比同类型方案具有更高的运算效率和通信效率.     

基于DSP的电网数据处理系统

介绍一种采用TMS320F2812和MAX125实现电网数据采集和处理,LabVIEW作为开发平台,并最终显示在LCD上的系统。三者之间通过RS 485总线实现数据通讯。给出了相关的基本原理和设计思想,并对该系统的硬件结构和软件设计中的关键部分进行了讨论。插值FFT和参数自适应算法的引入使该系统的测量精度进一步提高。实际应用表明,该系统能较好地完成对数据的处理。     

An Adaptive Smart Grid Management Scheme Based on the Coopetition Game Model

Recently, the idea of the smart grid has been gaining significant attention and has become a hot research topic. The purpose of this paper is to present a novel smart grid management scheme that uses game theory principles. In our proposed scheme, power appliances in the smart grid adaptively form groups according to the non‐cooperative hedonic game model. By exploiting multi‐appliance diversity, appliances in each group are dynamically scheduled in a cooperative manner. For efficient smart grid management, the proposed coopetition game approach is dynamic and flexible to adaptively respond to current system conditions. The main feature is to maximize the overall system performance while satisfying the requirements of individual appliances. Simulation results indicate that our proposed scheme achieves higher energy efficiency and better system performance than other existing schemes.     

基于大数据的智能电网信息调度算法改进

在大数据时代,随着信息技术的发展,电网在运行过程中会产生海量的数据,且数据类型逐渐多样化,这对电网有着更高的要求,需要电网向着数字化、智能化电网转型。文中主要阐述了大数据智能电网信息调度算法,对目前的多种大数据智能电网信息调度算法进行了分析,并对基于大数据的智能电网信息调度算法提出了改进策略。