Data encryption based on a 9D complex chaotic system with quaternion for smart grid

With the development of smart grid, operation and control of a power system can be realized through the power communication network, especially the power production and enterprise management business involve a large amount of sensitive information, and the requirements for data security and real-time transmission are gradually improved. In this paper, a new 9-dimensional(9D) complex chaotic system with quaternion is proposed for the encryption of smart grid data. Firstly, we present the mathematical model of the system, and analyze its attractors, bifurcation diagram, complexity,and 0–1 test. Secondly, the pseudo-random sequences are generated by the new chaotic system to encrypt power data.Finally, the proposed encryption algorithm is verified with power data and images in the smart grid, which can ensure the encryption security and real time. The verification results show that the proposed encryption scheme is technically feasible and available for power data and image encryption in smart grid.

Self-Attention Spatio-Temporal Deep Collaborative Network for Robust FDIA Detection in Smart Grids

False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal selfattention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.

Heterogeneous sensors data fusion method based on peak picking in probability density space

The multi-sensor multi-target localization and data fusion problem is discussed, and a new data fusion method called joint probability density matrix （JPDM） has been proposed, which can associate with and fuse measurements from spatially distributed heterogeneous sensors to produce good estimates of the targets. Based on probabilistic grids representation, the uncertainty regions of all the measurements are numerically combined in a general framework. The NP-hard multi-sensor data fusion problem has been converted to a peak picking problem in the grids map. Unlike most of the existing data fusion methods, the JPDM method does not need association processing, and will not lead to combinatorial explosion. Its convergence to the CRB with a diminishing grid size has been proved. Simulation results are presented to illustrate the effectiveness of the proposed technique.

基于人工智能的多源遥感数据融合在电网勘测应用研究

随着卫星遥感、空间科技等技术的不断发展,通过卫星采集多源遥感数据已被广泛应用到诸多行业。将多源数据进行融合,生成信息更丰富、质量更高的图像,能更清晰地分析地物地貌地形情况,已成为近年来图像智能处理领域的技术研究前沿方向。文章基于人工智能卷积网络和注意力机制,提出一种统一融合网络,将不同光谱和空间属性的遥感源数据进行有效融合,生成具有精确光谱信息和清晰空间细节的高分辨率图像,为电网勘测选址选线等业务提供了辅助支撑的有效手段。实验结果表明,文章研究结果比现有典型方法具备更好的融合效果。

基于多源数据深度融合的配电网运行评价方法

配电网作为供电环节中的最后一环,其运行水平决定了供电服务质量,因此配电网运行评价是供电单位的重要工作。但配电网运行评价工作受到跨业务数据资源难以融合、评价指标选择难以反映实际配电网运行状况等条件限制,现有评价方法难以实施应用。针对这一问题,提出一种基于多源数据深度融合的配电网运行评价方法,首先对区域配电网进行网格化划分,然后基于地址分级匹配算法对跨业务的多源异构数据进行深度融合。在此基础上,提取并设计配电网运行评价指标体系,构建基于多层权重约束的配电网运行综合评价模型,求解配电网综合评价结果。最后研发配电网运行评价系统,支持配电网各业务系统的多源异构数据的统一接入,实现配电网评价功能的可视化、智能化应用。研究成果配电网运行评价系统已采用某省级电网公司数据中台的相关数据进行配电网运行状态评估试验验证,以实际运行数据验证了所述方法的准确性和适用性。

基于网函数插值理论的结构表面风压预测

影响结构表面风荷载分布特性的因素很复杂,且具有不确定性,仅靠风洞试验难以完整地获得和描述其风荷载分布特性。因此,探讨了基于网函数插值理论的结构表面风压分布预测。首先,介绍了一种可多方向拟合一次误差调整的网函数插值法,并基于网函数插值技术对于多个传感器所获得的数据进行数据融合,提出了结构表面风压分布预测方法,然后用风洞试验数据进行了验证。实例验证结果表明:网函数插值法可以根据布置在结构物表面的测点网,快速计算出相邻位置的表面风压值,这是作为数据融合手段预测结构表面风压分布特性的一个有效途径,且该方法稳定性好,简单实用,精度较高,可被应用于风洞试验中减少测点数量和扩充复杂建筑表面风压分布数据等。

机理-数据融合驱动的互联变流系统故障诊断方法

三电平中点钳位型互联变流器,因其大容量、高电能质量等优势,已逐渐成为交直流混联配电网系统的主流能量变换装备。但其长期工作于大功率、时变负载、有限散热等恶劣工况,功率器件开路故障率高。同时,现有故障诊断方法多为单一机理或数据依赖,无法克服系统模型结构复杂、运行工况多变的难题,诊断准确性与快速性较差。为此,提出了一种机理-数据融合驱动的互联变流系统故障诊断方法。首先,基于三电平变流系统的机理模型,结合神经网络观测器,构建机理-数据融合模型以提高故障诊断精度。随后,分析了电流残差量在不同器件开路故障后的变化轨迹,总结出电流残差故障特征表进行开路故障诊断。最后,实验与硬件在环测试结果验证了所提方法的有效性。

电网工程在BIM环境下数字化建设管理模式研究与应用

国家电网工程建设信息化管理模式主要以项目建设业务流为主线,当前BIM技术的引入给建设管理带来新的手段和方式。分析传统信息化管理模式的特点,遵循实用、无感、可用、智能、整体五个基本原则,重新设计电网工程在建筑信息模型(building information modeling,BIM)环境下数字化建设管理模式,提出“电网工作包”的新理念,设计新模式下数字化管理平台的整体框架,对比分析两种管理模式的应用差异并在某新区电网进行实现和应用,验证新管理模式能有效提升电网建设管理水平。

基于模糊控制理论集的自适应数据融合与修正研究

电网电能传输的可靠性和稳定性是行车安全的保障,由于外部干扰波动和电流电弧对电网结构的影响,其参数获取和动态建模变得困难。首先,分析电网结构失真度和模型结构变化的特点,采用基于理论集的模糊控制分析电网动态电能传输的特点。其次,对于采样电流的多变模型进行数据融合,结合自适应控制的策略,在线修正误差超调和抑制波动,并寻求模糊控制约束保持其局部最小的误差均方值稳定。最后,通过实验参数建立Simulink模糊仿真模型验证了自适应数据融合技术能有效地修正误差和波动,保证良好的行车电能传输。

新型电力系统多源异构数据融合技术研究现状及展望

能源转型背景下,新型电力系统以清洁低碳、开放互动为目标不断建设,同时监测技术与通信技术也快速发展,电力系统中的数据来源更加广泛,数据结构更加复杂,为新型电力系统数据融合提供数据基础的同时也提出了挑战。首先,分析新型电力系统数据特征,提出新型电力系统的数据融合需求;接着,介绍新型电力系统数据模型、多源异构数据融合技术层级,分析关键融合技术的优缺点,并对不同技术的适用场景进行分析;然后,分别从输配协同、源网荷储协同、虚拟电厂、多元负荷、电碳市场交易5个典型场景,对多源异构数据融合的数据需求、数据来源、融合目标、常见方法及研究难点进行归纳;最后,对新型电力系统数据融合技术的未来研究发展进行了展望。

基于阵风系数模型的百米级阵风客观预报算法研究

京津冀地区经济和文化的快速发展对冬季地面瞬时强风预报要求越来越高。正确估计和预测冬季地面瞬时强风,尤其是复杂地形条件下的阵风高分辨率格点精准预报,对于提升重大活动服务保障、首都及周边地区城市安全运行及防灾减灾能力等方面都具有重要意义。本研究基于京津冀长时间序列的实况观测资料,建立了阵风系数与稳定风速、风向、地形高度各要素之间的关系模型,并结合客观统计分析方法、阵风观测数据融合技术、格点偏差订正技术,发展了一种既保留模式物理参数特征和阵风局地气候特征,又发挥格点偏差订正技术的阵风客观预报方法。冬季奥林匹克赛事期间批量检验和个例分析结果表明,基于阵风系数格点模型和模式后处理订正技术得到的百米级分辨率、分钟级更新的阵风客观预报产品,24 h预报时效内张家口赛区和延庆赛区考核站平均绝对误差分别在2.3 m/s和3.0 m/s以下,延庆赛区8级以上大风,阵风风速预报评分超过0.5,解决了复杂山区数值模式阵风预报误差大、几乎无法业务应用的瓶颈问题,满足冬季奥林匹克运动会现场服务要求。

基于边缘计算的智能电网多源异构数据监测

随着信息技术的不断发展,供电网络中边缘设备的数据量迅速增加,对数据处理的实时性和传输带宽提出了更高要求。建立了配电网安全风险图,通过势函数对安全风险进行计算分析,得到节点势值和关键节点等数据;基于边缘计算建立智能电网多源异构数据监测模型,可以实现配电网故障的及时感知和实时响应,缩短停电时间,提高了配电网的供电可靠性和用户满意度。以某地区配电网线路为例,对节点互相作用及关联性进行计算和对比,验证了势函数在边缘计算风险评估中的有效性,能够为配电网故障诊断提供理论指导。

基于CIM/SVG的电力系统图模数据融合技术

针对调度主站和电网GIS平台图模数据源不统一、图模规范不一致,导致电力系统输变配拓扑无法贯通及图模维护工作量大的问题,提出了一种基于CIM/SVG的电力系统图模数据融合技术。依据图模交互规范梳理图模校验规则,采用CIM解析图模,运用SVG分析中间结构拓扑。开发图模数据融合系统实现数据维护源头统一,支撑输变配一张网的拓扑贯通与集成应用。通过对多个地市局不同电压等级大量变电站样例图模接入,结果表明所提技术的正确性及可行性,有效解决了输变配图模数据融合的问题。

Development and Evaluation of Hourly and Kilometer Resolution Retrospective and Real-Time Surface Meteorological Blended Forcing Dataset(SMBFD) in China

A real-time,long-term surface meteorological blended forcing dataset(SMBFD)has been developed based on station observations,satellite retrievals,and reanalysis products in China.The observations are collected at national and regional automatic weather stations,satellite data are obtained from the Fengyun(FY)series satellites retrievals,and the reanalysis products are obtained from the ECMWF.The 90-m resolution digital terrain elevation data in China are obtained from the Shuttle Radar Topographic Mission(SRTM)for temperature and humidity elevation adjustment.The dataset includes 2-m air temperature and humidity,10-m zonal and meridional winds,downward shortwave radiation,surface pressure,and precipitation.The spatial resolution is 1 km,and the temporal resolution is 1 h.During the data processing procedure,various data fusion techniques including the space–time multiscale variational analysis,the discrete ordinates radiative transfer(DISORT)model,the hybrid radiation estimation model,and a terrain correction algorithm are employed.Dependent and independent evaluations of the dataset are performed against observations.The SMBFD dataset is also compared with similar datasets produced in other major meteorological operational centers in the world.The results are as follows.(1)All variables show reasonable geographic distribution features and realistic spatial and temporal variations.(2)Dependent and independent evaluations both indicate that the gridded SMBFD dataset is close to the observations,while the dependent evaluation yields better results than the independent evaluation.(3)Compared with similar datasets produced in other meteorological operational centers,the real-time and retrospective surface meteorological fusion data obviously have higher quality.The dataset introduced in the present study is in general stable and accurate,and can be applied in various practice such as meteorology,agriculture,ecology,environmental protection,etc.Meanwhile,this dataset has been used as the atmospheric forcing data to drive the operational High-resolution Land Data Assimilation System of China Meteorological Administration.The dataset with the network Common Data Form(NETCDF)can be decoded by various programming languages,and it is freely available to non-commercial users.

长三角跨区域北斗位置应用服务模型设计及应用

为做好长三角卫星导航定位基准服务系统的互联互通,跨区域融合三省一市北斗地基增强系统资源,提升北斗位置服务支撑保障能力,本文研究了轻量化惰性格网和地理围栏技术,统一了跨区域差分数据格式,设计了区域一体化的北斗位置应用服务模型。通过浙江省卫星导航定位基准服务“政务一朵云”发布验证了轻量化惰性格网的高效性,并结合太湖西岸的跨区漫游实例验证了相关服务的有效性。

基于知识图谱的电网调度运行优化管理系统

为应对电网安全态势复杂多变,缓解现行调控管理规范化水平不足、数据利用效率低下等问题,文章提出基于知识图谱的电网调度运行优化管理系统,并从多模态数据融合、信息流关联推理、电网脑态势解析、智能体辅助决策等方面阐述融合知识图谱的电网调度运行管理系统的优化机制;结合智能调度新需求,提出基于知识图谱的态势“三高”评估、负荷“三多”推荐、故障“三化”解析等优化管理方法全面提升安全稳定运行管理水平以及工作效率。依托大电网智能调控平台,将系统应用在电网调度运行管理中进行效果展示,全面反映电网运行状态和整体概况,提升电力系统内部知识的综合利用率,保障电网安全稳定运行。

基于多源数据融合感知的电网设备供应链管控与预警算法设计

针对精密特种电网设备的运输监测问题,开展了面向电网设备供应链的多源数据融合感知算法研究。构建了电网设备供应链的多源数据融合感知系统架构,提出了基于深度置信网络(DBN)-Dempster Shafer(DS)证据理论的电网设备运输状态感知算法。同时利用该算法分别对来源于加速度、倾角及温湿度等多传感器的数据进行特征提取,通过DS证据理论对不同特征信息进行融合分析,以得到电网设备运输状态融合感知结果。算例分析结果表明,所提算法结合深层神经网络与决策层数据融合,能够深入挖掘多源数据之间的内在关联,且在电网设备运输状态感知上具有更高的准确性。在实际电网设备供应链监控中,其可准确监测电网设备的倾斜状态,并提高电网设备供应链的运输质量与安全水平。

基于自编码神经网络的电网故障预测算法

针对电力设备因素、人为因素与气象因素导致的电网故障问题,提出了一种基于自编码神经网络的电网故障预测算法.利用电网运维数据与气象数据来预训练自编码网络,以提取其特征及不同数据间的关联关系.基于多级别特征融合和预测网络来构建各影响因素与电网故障间的映射,根据当前电网环境得到电网故障类型,并使用稀疏正则项来增强网络的鲁棒性.仿真与算例分析结果表明,所提出的算法能够提取出丰富的气象特征,并准确预判电网在给定条件下是否会发生故障及发生故障的概率.

A fine-grained privacy protection data aggregation scheme for outsourcing smart grid

Compared with the traditional power grid,smart grid involves many advanced technologies and applications.However,due to the rapid development of various network technologies,smart grid is facing the challenges of balancing privacy,security,efficiency,and functionality.In the proposed scheme,we design a privacy protection scheme for outsourcing smart grid aided by fog computing,which supports fine-grained privacy-protected data aggregation based on user characteristics.The fog server matches the encrypted characteristics in the received message with the encrypted aggregation rules issued by the service provider.Therefore,the service provider can get more fine-grained analysis data based on user characteristics.Different from the existing outsourcing smart grid schemes,the proposed scheme can achieve real-time pricing on the premise of protecting user privacy and achieving system fault tolerance.Finally,experiment analyses demonstrate that the proposed scheme has less computation overhead and lower transmission delay than existing schemes.

电网系统高效数据融合通信技术研究

针对网络通信方案无法满足电网系统大量设备接入和数据高速分析需求的现状,提出了一种新的电网系统高效数据融合通信技术,其采用了STM-1高容量数据传输方案,满足了电网数据主站的控制数量及数据传输容量,采用多控制终端聚合传输技术,解决了电力通信网络覆盖范围小的不足。实际应用表明,该高效数据融合通信技术能够将数据通信效率提升93.4倍,对提高电网系统的运行稳定性和可靠性奠定了基础。