Hybrid Scalable Researcher Recommendation System Using Azure Data Lake Analytics

This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].

基于Power BI的螺栓拧紧数据智能管理系统

为保证整车装配的安全和质量,需要采用大量带监控拧紧工具完成整车零部件的装配工作,并借助拧紧数据完成拧紧工作的日常管理。然而,该过程产生的数据量庞大,传统方式采用人工统计分析拧紧数据,存在统计效率低下、数据处理工作繁琐、可视化效果不佳等问题。为了解决该问题,文章基于智能分析软件Power BI对拧紧数据进行建模处理,并搭建螺栓拧紧数据智能管理系统,实现拧紧数据的自动化管理和分析。该系统包含了海量拧紧数据的自动加载、自动统计和自动生成可视化报表等功能,业务人员可以随时访问系统查询报表,大幅提升了现场拧紧管理的工作效率。

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.

Power Quality Data Compression Based on Iterative PCA Algorithm in Smart Distribution Systems

To reduce the stress of data transmission and storage for power quality (PQ) in smart distribution systems and help PQ analysis, a multichannel data compression based on iterative PCA (principal component analysis) algorithm is introduced. The proposed method uses PCA to reduce the redundancy of data to achieve the purpose of compressing data. In order to improve the calculating speed, an iterative method is proposed to compute the principal components of the covariance matrix. The correctness and feasibility of the proposed method are verified by field PQ data tests. Compared with discrete wavelet transform (DWT) method, the proposed method has good performance on compression ratio and reconstruction accuracy.

Hybrid Power System Power Flow Analysis

Hybrid power supply system consists of a number of independent and different sources of electrical energy with different operating times during different seasons and with energy storage system. Deployment of a hybrid power system is expected in places outside the normal distribution network. For the further research and improvements it is necessary to know in detail the power flow from various sources to the load or to storage battery depending on different seasons. The paper presents data analysis results computed by application developed for detailed analysis of power flows within hybrid power system. Developed application analyses the data from the monitoring system. Data has been acquired and recorded within last year. This data is visualized as power flows in the individual hybrid power system circuits. Together with electrical power the effectiveness and performance parameters of rectifier and DC/AC converter are evaluated. The paper describes achieved results and needs for further improvements of such solution.

新型电力系统数据跨域流通泛安全边界防护技术

新型电力系统建设涉及多业务系统、多部门、多方主体间进行海量、异构数据的交互和共享,电力数据的内外部网络环境与安全形势日趋复杂化,数据流通的脆弱性风险加剧。首先,分析新型电力系统下数据流的类型与特性,概括电力数据流通安全防护面临的新形势;其次,基于专用数据处理器(DPU)的高性能流量编排和多功能安全网关能力,构建面向电力数据跨域流通安全增强的泛安全边界,凭借数据面可编程技术沟通网络安全与数据安全双维度安全能力,提出基于DPU的数据跨域流通协同防护技术应用方案;最后,阐释DPU在不同电力通信网络层次的部署方式、价值与关键技术,分析现阶段DPU在电力领域应用存在的挑战。

Research on Optimization for Units Start During Power System Restoration

Considering units starting and network constraints and the concept of optimization period,a optimization model which is a typical multi-constraint knapsack problem is established to solve the selection optimization problem of units starting in power system restoration period in this paper, and the objective of the model is to maximize the total power generation capability. A relative effectiveness assessment based on a improving data envelopment analysis is adopted to select the initial units to be started, genetic algorithms are employed to solve the knapsack problem to determine the most reasonable units be started at the current time. Finally, IEEE-39 bus system simulation result proves that the proposed model is feasible and effective.

Improvements of the Computerized Data Acquisition System for 25MWt Experimental Facility of Coal-fired MHD Generator

This paper introduces the design and development of a new computerized data acquisition system for the coal fired magnetohydrodynamical (MHD) electrical power generation experiments. Compared to the previous system, it has a higher sampling rate and an improved simultaneity performance. It also improves the data collection method and sensor design for the measurement of Faraday voltages and Faraday currents. The system has been successfully used in many regular MHD generator tests. It provides an excellent base for the future research and development of the Coal fired MHD electrical power generation.

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.

A Cooling System with a Fan for Thermal Management of High-Power LEDs

To improve the heat dissipation of high-power light-emitting diodes (LEDs), a cooling system with a fan is proposed. In the experiment, the LEDs array of 18 W composed of 6 LEDs of 3 W is used and the room temperature is 26oC. Results show that the temperature of the substrate of LEDs reaches 62oC without the fan, however, it reaches only 32oC when the best cooling condition appears. The temperature of the LEDs decreases by 30oC since the heat produced by LEDs is transferred rapidly by the fan. The experiment demonstrates that the cooling system with the fan has good performance.

基于自适应生成对抗网络的智能电网状态重构的虚假数据攻击检测

考虑到电力系统与能源互联网的深度耦合,虚假数据注入攻击对电力系统的威胁不断提升。文章针对虚假数据注入攻击(false data injection attack,FDIA)设计自适应生成对抗网络(adaptive generative adversarial networks,AGAN)状态重构的虚假数据注入攻击检测方法。该方法在生成对抗网络(generative adversarial networks,GAN)基础上融入卷积神经网络(convolutional neural network,CNN)以及自适应约束下的自注意力机制(self-attention,SA),实现节点间全局参考性,从而实现状态的有效重构和异常状态的准确预测;根据AGAN的异常数据预测结果设计结合网络判别值的检测逻辑。最后,在IEEE14节点的电力系统上验证所提方法的有效性,且对比GAN、CNN,AGAN重构的平均绝对百分比误差为0.0001%,检测准确率可达到98%。

面向设计研发的核能大数据系统架构及标准化研究

结合核能设计研发基本流程和全生命周期数据特点,开展基于多源异构数据集成的采集、存储、处理、管理、应用等功能需求分析,按照“分层设计”思想和以数据应用为目标的路线,建立核能大数据系统技术架构和数据架构,对架构中的层级、模块、功能进行分析。考虑大数据技术持续应用的特点,基于核能大数据系统架构和技术发展需求,开展标准化研究,提出核能大数据标准体系。该研究为大数据技术在核能设计研发应用提供基础,并为后续开展系统性的核能大数据标准化工作提供技术参考。

大数据背景下的电力营销信息管理平台设计

为了提升电力营销大数据处理和调度效率,设计大数据背景下的电力营销信息管理平台。基于Hadoop和MapReduce设计平台存储和技术框架,利用大数据技术划分电力营销大数据,实现电力营销信息状态安全分析和监控;采用云计算技术分析和处理电力营销数据。测试结果显示,所设计平台的大数据处理吞吐量较高、大数据处理加速比稳定;数据调度计算效率较高、性能稳定;将电力营销数据以对象形式缓存到内存中时,执行时间较短、耗费内存较小。该平台具备较好的数据处理及调度能力。

Data Logging and Control of a Remote Inverter Using LoRa and Power Line Communication

For decades, the power system was highly centralized. With the growing integration of distributed generations into the system, there is a necessity for bi-directional communication methods to monitor and control the remote assets. The primary objective of this paper is to develop a communication link for monitoring and controlling a grid-connected inverter in a remote location. Furthermore, the paper presents developments that have been incorporated to improve the communication link. The literature survey indicates that LoRa is superior compared to other technologies, but has some security and reliability issues. This paper also presents an encryption algorithm to improve the security of the LoRa link. Local data storage added to the system before transmitting data increases the system reliability. A display at the transmission end is added to improve the user-friendliness of the communication link. A Powerline Communication link is parallelly added to the LoRa link to improve the reliability. Finally, tests are conducted with an actual inverter and the results are presented. The tests show that the developed communication link has improved security and reliability, while its open nature makes it highly scalable and adaptable for employment in other smart grid applications.

论数字化时代的社会公权力及数字立法范式转变

自创生系统论指出,社会公权力具有主体的社会性、内容的公共性、来源的事实性,并为数字立法范式转变提供了理论基础。数字化时代社会公权力具有利维坦式权力风险,数据在其形成过程中具有决定性作用。数字化时代社会公权力的本质在于数据成为形构数字社会的基本构成要素。通过风险探究与本质考察发现,除具备工业化时代社会公权力的一般特质外,数字化时代社会公权力还具有权力产生上的数据化,权力表现形式上的场域化、多元化、去中心化,权力运行方式上的监视性与规训性之特点。对数字化时代社会公权力的规制还是要回到自创生系统论的思路,根据其上述特点,推动数字立法范式的转变。

基于环境激励的电力系统静态电压稳定在线评估

随着电网结构的日益复杂,传统的静态电压稳定分析难以满足速度和准确度的要求。为此,本文提出一种基于随机响应数据的静态电压稳定在线评估方法。首先,推导基于节点电压和线路无功功率的灵敏度指标,在深入分析环境激励下灵敏度指标特性的基础上,提出对灵敏度进行分类;然后,引入K均值异常检测算法处理异常的灵敏度数值,有效地改善了灵敏度估计的准确性;其次,采用加权平均值计算一段时间内的灵敏度,实时判断系统的静态电压稳定性。最后,在IEEE10机39节点系统上进行验证,结果表明所提方法能够准确监测电力系统静态电压的稳定性。

电力人工智能的演变与展望——从专业智能走向通用智能

新型电力系统快速发展背景下,海量多源异构信息与多类型业务深度耦合,电力系统运行面临着强复杂性、随机性等挑战。同时,加快构建灵活智能的新型电力系统是能源发展的重要战略,亟须形成具备智慧性、自适应性、安全性的电力人工智能技术体系,推动新型电力系统智能化转型发展。文中对电力人工智能技术的演变过程与研究现状进行回顾总结;分析了以预训练多模态大模型为基础的新一代电力人工智能(AI EPS)的技术框架、原理与关键技术方法;提出了电力大模型技术在电力系统感知预测、调控决策与运行规划等场景的应用方案;阐述了基于电力大模型的电力人工智能面临的技术难点与应用瓶颈。最后,对电力通用人工智能技术应用进行了总结与展望。