Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

Due to the high inherent uncertainty of renewable energy,probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities.However,the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data.This article proposes a physics-informed artificial intelligence(AI)surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance.The incomplete dataset,built with numerical weather prediction data,historical wind power generation,and weather factors data,is augmented based on generative adversarial networks.After augmentation,the enriched data is then fed into a multiple AI surrogates model constructed by two extreme learning machine networks to train the forecasting model for wind power.Therefore,the forecasting models’accuracy and generalization ability are improved by mining the implicit physics information from the incomplete dataset.An incomplete dataset gathered from a wind farm in North China,containing only 15 days of weather and wind power generation data withmissing points caused by occasional shutdowns,is utilized to verify the proposed method’s performance.Compared with other probabilistic forecastingmethods,the proposed method shows better accuracy and probabilistic performance on the same incomplete dataset,which highlights its potential for more flexible and sensitive maintenance of smart grids in smart cities.

Economical Optimization of Grid Power Factor Using Predictive Data

We present an electrical grid optimization method for economical benefit. After simplifying an IEEE feeder diagram, we build a compact smart grid system including a photovoltaic-inverter system, a shunt capacitor, an on-load tapchanger(OLTC) and transmission lines. The system power factor(PF) regulation and reactive power dispatching are indispensable to improve power quality. Our control method uses predictive weather and load data to decide engaging or tripping the shunt capacitor, or reactive power injection by the photovoltaic-inverter system, ultimately to keep the system PF in a good range. From the perspective of economics, the economical model is considered as a decision maker in our predictive data control method.Capacitor-only control strategy is a common photovoltaic(PV)regulation method, which is treated as a baseline case. Simulations with GridLAB-D on profiled loads and residential loads have been carried out. The comparison results with baseline control strategy and our predictive data control method show the appreciable economical benefit of our method.

Research and Application of Distributed Data Mining Method for Improving Rural Power Grid Enterprises in Production and Operation Status Evaluation

With the reform of rural network enterprise system,the speed of transfer property rights in rural power enterprises is accelerated.The evaluation of the operation and development status of rural power enterprises is directly related to the future development and investment direction of rural power enterprises.At present,the evaluation of the production and operation of rural network enterprises and the development status of power network only relies on the experience of the evaluation personnel,sets the reference index,and forms the evaluation results through artificial scoring.Due to the strong subjective consciousness of the evaluation results,the practical guiding significance is weak.Therefore,distributed data mining method in rural power enterprises status evaluation was proposed which had been applied in many fields,such as food science,economy or chemical industry.The distributed mathematical model was established by using principal component analysis(PCA)and regression analysis.By screening various technical indicators and determining their relevance,the reference value of evaluation results was improved.Combined with statistical program for social sciences(SPSS)data analysis software,the operation status of rural network enterprises was evaluated,and the rationality,effectiveness and economy of the evaluation was verified through comparison with current evaluation results and calculation examples of actual grid operation data.

Operating Analysis and Data Mining System for Power Grid Dispatching

The dispatching center of power-grid companies is also the data center of the power grid where gathers great amount of operating information. The valuable information contained in these data means a lot for power grid operating management, but at present there is no special method for the management of operating data resource. This paper introduces the operating analysis and data mining system for power grid dispatching. The technique of data warehousing online analytical processing has been used to manage and analysis the great capacity of data. This analysis system is based on the real-time data of the power grid to dig out the potential rule of the power grid operating. This system also provides a research platform for the dispatchers, help to improve the JIT (Just in Time) management of power system.

Using Grey Target Theory for Power Quality Evaluation Based on Power Quality Monitoring Data

Smart grid puts forward higher requirements for power quality.Power quality evaluation can provide a decision-making basis for quality improvement.Based on power quality monitoring data,a grey target method is proposed for power quality evaluation.The grey target is composed of power quality evaluation standard and power quality monitoring data to be evaluated.Combining with the characteristics of each power quality evaluation index,the target center of the whole grey target system is found.Then,the power quality monitoring data to be evaluated and the power quality standard mode are compared and analyzed to construct the power quality grey correlation difference information space.Finally,the target center coefficient and target degree of power quality are calculated to realize the comprehensive evaluation of power quality,and the evaluation grade of power quality monitoring data to be evaluated is obtained.Compared with the evaluation results of the existing literature,the effectiveness of the proposed method is verified,which shows that grey target theory is reasonable in the comprehensive evaluation of power quality.

Forecast of Power Generation for Grid-Connected Photo-voltaic System Based on Grey Theory and Verification Model

Being photovoltaic power generation affected by radiation strength, wind speed, clouds cover and environment temperature, the generating in each moment is fluctuating. The operational characteristics of grid-connected PV systems are coincided with gray theory application conditions. A gray theory model has been applied in short-term forecast of grid-connected photovoltaic system. The verification model of the probability of small error will help to check the accuracy of the gray forecast results. The calculated result shows that the ?model accuracy has been greatly enhanced.

基于环境中移动运输代理的传感器网络建模

针对大型稀疏传感器网络中的数据获取,本文提出了一种利用环境中普遍存在的移动代理来连接稀疏传感器的网络体系结构和一种2-维网格随机游走分析模型;提出的传感器网络模型由3个抽象层构成,即由无线传感器构成的底层、由各种运输代理构成的中间层和由接入点/中央存储库构成的顶层。具体实现原理是位于中间层的移动运输代理从底层分布的无线传感器收集数据并缓冲数据,然后经过游走运输,最后将从底层的无线传感器收集的数据交付到顶层必要的接入点进行必要的存储和处理,从而实现整个传感器网络的数据获取;理论分析和仿真实验结果表明,提出的基于移动运输代理的传感器网络模型不仅具有较好的鲁棒性和可扩展性,而且相比于基站网络模型和Ad-hoc网络模型,在传感器功率消耗、数据成功率和基础设施投入成本方面有明显的优势。

电能质量监测功能在数字化电网监测平台中的实现方法

文章介绍了电能质量监测功能在基于信息通信技术的数字化电网监测平台中的实现方法。数字化电网监测平台的数据采集方式为标准数字化采样,传统电能质量检测算法无法直接利用该采样数据。对采样数据进行动态线性插值处理,可满足频率跟踪及FFT计算的需求。对研究结果进行了测试,满足国标精度要求。

电网数字孪生分析模型建模及应用

为支撑新一代调度技术支持系统全局分析计算业务融合,提出一种基于数据孪生的电网分析模型构建方法。基于实际电网设备的物理和逻辑关系,构建出标准化、对象化的完整电网分析模型,并接入实时量测,使其成为真实空间物理电网的数字孪生。在传统通用数据库与专业计算应用间构建出更贴近电网分析计算业务的数据中台,为在线分析计算应用提供统一数据服务。通过在新一代调度支持系统华中试点工程的建设成效,展示了基于数字孪生模型的分析计算应用的性能提高效果。

基于多源异构数据融合的造价数据与GIM模型挂接方法

针对电网工程构件的GIM模型无法包含造价分析所需要的所有信息,导致造价分析精度及效率较低的问题,提出了一种基于多源异构数据融合的造价数据与GIM模型的挂接方法。该方法建立了电网工程GIM构件属性集和造价数据库,并从中提取出构件本体来实现GIM构件到本体间的映射以及造价数据到本体间的映射关系,进而完成多源异构的造价数据与GIM模型间的挂接。仿真实验及分析结果表明,所提方法能够大幅节省造价分析人员的查询和计算时间,从而提高电网工程造价分析与管理的效率。

基于MapReduce的并行化电网运行数据处理方法研究

在大规模新能源的新型电力系统中,由于云端电力数据存在结构复杂、数据量大及多维度的特点,因此不利于发挥数据对运行的指导作用,甚至有可能危害电力系统的安全、稳定运行。针对上述问题,文中提出了一种基于MapReduce的电网数据分析方法。其将云计算应用于新型电力系统,并构建了基于MapReduce云计算模型的并行化处理算法,进而提升了系统的响应速度。通过将该方法应用于电网的数据处理结果表明,所提方法可以有效地提高电网运行数据处理的准确性和工作效率。在海量数据的工况下,其处理效率约为30 min,且随着数据量的增加仍可保持稳定性与准确性,实现了网格化的并行分析。

基于支持向量机的网格化电网负荷预测算法设计

针对电网负荷预测算法预测能力较差、效率偏低的问题,文中提出了一种PCA-PSO-SVM算法。其在经典粒子群算法的基础上引入主元分析法,使模型具有降低数据维度及算法冗余度的特性。同时通过改进的PCA-PSO算法对SVM模型的内置参数进行最优选取,从而使改进后的SVM模型具有最佳的分类性能。在实验测试中,采用PCA算法选取了91%贡献度内的6项数据特征进行样本数据训练。结果表明,相较于其他对比算法,该文算法预测结果的RMSE、MAE与MAPE误差值均为最小,证明其可对网格化电网负荷加以预测。此外,该算法还能提升传统算法的准确度,为电力负荷分配提供有力支持。

An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids

With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.

基于深层级联残差图卷积的暂态稳定评估模型及其实际电网应用

目前,数据驱动的暂态功角稳定评估模型的研究和测试主要在小规模算例系统进行,在实际电网的应用检验不足,其内在原因是图深度学习中的局部信息提取特点与功角稳定全局性的矛盾尚未解决。为此,设计一种深层级联残差图卷积模型,利用含残差连接的深层级联结构,实现模型层数堆叠性能的有效提升。利用MinMaxPooling模块,使模型参数与系统规模解耦。该模型结构设计与节点数量无关,可以解决数据驱动模型应用于大规模实际电网的问题。在某5419个节点的实际区域电网进行测试,结果验证了所提模型的有效性和实用性。

基于知识图谱技术的电网项目数据关联分析

为解决电网项目数据关联难度大的问题,提出了基于知识图谱技术的电网项目数据关联分析。分析了历史项目特征提取和新增储备项目特征提取方法,论述了基于规则的信息抽取和基于序列标注的信息提取方法,以及电力项目知识图谱的基本构成和构建方法,提出了项目多维关联分析平台,论述了分析方法和分析流程,并提出了系统网络结构,结合实际应用说明了所提系统的应用效果。

智能电网技术在电力系统中的应用与发展趋势

智能电网技术是基于通信技术、数据分析技术、自动化和控制技术,实现电力系统的高效、可靠和可持续运营。该文系统论述了智能电网技术在电力需求预测、电力生产和分配、电力系统监测等方面的应用,分析智能电网技术在电力系统中的应用优势与局限条件,并提出未来发展趋势,不断提高信息输送的安全性、隐私性和稳定性,不断推动技术创新,旨在为推动电力行业朝着更智能、更环保和更可持续的方向发展提供技术参考与借鉴。

核电厂及周边辐射环境航空监测空地比对方法研究

核电厂辐射环境航空监测中进行空地比对是验证航测数据质量好坏的重要手段和方法,本次采用面积网格法,在研究区内的第四系沉积物区、基岩地层区和偏离测线及海陆交汇区域,选取多个监测点位开展空地比对分析,研究地面测量方法的有效性及位置选择的合理性。结果显示:第四系沉积物区监测比对点相对偏差在-8.8%~9.7%之间,基岩地层区监测比对点相对偏差在-9.1%~15.8%之间,偏离测线和海陆交汇区域比对点相对偏差在3.0%~44.3%之间。研究表明:面积网格法在辐射环境航空监测的空地比对研究工作中具有较好的应用性;在第四系沉积物区、基岩地层区域开展空地比对研究效果较好,可作为辐射环境航空监测空地比对地面选点的依据;受较多因素影响,偏离测线和海陆交汇区域比对效果较差,后续开展相关空地比对工作要重点研究和考虑。本次研究可为核设施辐射环境航空监测技术研究提供参考思路,也可为准确分析核设施及周边辐射环境变化趋势、核素迁移规律、核事故应急响应提供基础数据研究分析方法。

上海轨道交通“新三线”综合监控系统电力数据传输方案优化

[目的]上海轨道交通“新三线”(14、15、18号线)综合监控系统深度集成电力系统,综合监控系统与电力网调平台共有设备传输电力数据,存在诸多问题,需优化综合监控系统电力数据传输方案。[方法]分析了国内各地轨道交通以及上海轨道交通“新三线”的综合监控系统电力监控模式,分析了优化前上海轨道交通“新三线”综合监控系统电力数据传输方案及易导致的问题,介绍了上海轨道交通“新三线”综合监控系统电力数据传输优化方案。[结果及结论]优化方案为:增加专用的电力接口服务器,将电力网调平台所收的电力数据从原先的接口服务器中剥离出来;采用双链路传输,不改变既有的经综合监控系统传输电力数据的方式,经电力网调平台电力数据采用独立链路转发。采用优化方案,能够提高综合监控系统、电力网调平台以及其他业务平台的检修效率,同时可降低故障对其他业务的影响。

云边协同背景下基于融合RF算法的电网数据资产综合处理技术

针对现有大多数方法难以充分挖掘出电网数据潜在价值的问题,提出了一种云边协同背景下基于随机森林算法结合BP神经网络的电网数据资产综合处理技术。该技术在靠近电网数据源一侧部署边缘计算节点,以构建云边协同环境下的电网数字化资产管理系统。利用随机森林算法设计分类器完成电网数据类型的划分,并将各类型数据输入至BP神经网络中进行学习,通过不断地迭代优化输出相应的综合处理结果。基于Python平台进行的实验分析结果表明,所提技术的分类准确率均超过了90%,能够有效提升电网数据资产的处理效率。

电力数据网络设备运行优化

为增强电网数据通信的安全性,巩固变电站网络运行的稳定性,需要对电力数据网络设备进行优化,文章详述调度数据设备和传输制造报文规范(manufacturing message specification,MMS)信息交换机的优化方案,涉及路由器限制明文流量的访问控制列表(accesscontrollist,ACL)优化、路由器ACL策略配置书写优化、调度数据设备组网模式优化、MMS交换机级联模式优化以及MMS交换机业务分区优化。除优化后符合调度设计及网络安全原则的方案以外,其余方案结合调控中心平台监视和模拟实验,对优化成效进行验证,结果表明方案实现了电力数据网络的持续健康运行。