Real Time Control System for Metro Railways Using PLC & SCADA

This paper proposes to adopt SCADA and PLC technology for the improvement of the performance of real time signaling&train control systems in metro railways.The main concern of this paper is to minimize the failure in automated metro railways system operator and integrate the information coming from Operational Control Centre(OCC),traction SCADA system,traction power control,and power supply system.This work presents a simulated prototype of an automated metro train system operator that uses PLC and SCADA for the real time monitoring and control of the metro railway systems.Here,SCADA is used for the visualization of an automated process operation and then the whole opera-tion is regulated with the help of PLC.The PLC used in this process is OMRON(NX1P2-9024DT1)and OMRON’s Sysmac studio programming software is used for developing the ladder logic of PLC.The metro railways system has deployed infrastructure based on SCADA from the power supply system,and each station’s traction power control is connected to the OCC remotely which commands all of the stations and has the highest command priority.An alarm is triggered in the event of an emergency or system congestion.This proposed system overcomes the drawbacks of the current centralized automatic train control(CATC)system.This system provides prominent benefits like augmenting services which may enhance a network’s full load capacity and networkflexibility,which help in easy modification in the existing program at any time.

A call for enhanced data-driven insights into wind energy flow physics

With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations,machine learning(ML)models are poised to advance our understanding of the physics underpinning the interaction between the atmospheric boundary layer and wind turbine arrays,the generated wakes and their interactions,and wind energy harvesting.However,the majority of the existing ML models for predicting wind turbine wakes merely recreate Computational fluid dynamics(CFD)simulated data with analogous accuracy but reduced computational costs,thus providing surrogate models rather than enhanced data-enabled physics insights.Although ML-based surrogate models are useful to overcome current limitations associated with the high computational costs of CFD models,using ML to unveil processes from experimental data or enhance modeling capabilities is deemed a potential research direction to pursue.In this letter,we discuss recent achievements in the realm of ML modeling of wind turbine wakes and operations,along with new promising research strategies.

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.

Designing an Intelligent Control Philosophy in Reservoirs of Water Transfer Networks in Supervisory Control and Data Acquisition System Stations

In this paper, a hybrid neural-genetic fuzzy system is proposed to control the flow and height of water in the reservoirs of water transfer networks. These controls will avoid probable water wastes in the reservoirs and pressure drops in water distribution networks. The proposed approach combines the artificial neural network, genetic algorithm, and fuzzy inference system to improve the performance of the supervisory control and data acquisition stations through a new control philosophy for instruments and control valves in the reservoirs of the water transfer networks. First, a multi-core artificial neural network model, including a multi-layer perceptron and radial based function, is proposed to forecast the daily consumption of the water in a reservoir. A genetic algorithm is proposed to optimize the parameters of the artificial neural networks. Then, the online height of water in the reservoir and the output of artificial neural networks are used as inputs of a fuzzy inference system to estimate the flow rate of the reservoir inlet. Finally, the estimated inlet flow is translated into the input valve position using a transform control unit supported by a nonlinear autoregressive exogenous model. The proposed approach is applied in the Tehran water transfer network. The results of this study show that the usage of the proposed approach significantly reduces the deviation of the reservoir height from the desired levels.

基于多层级时空图神经网络的风电机组在线异常检测

在风电场运营中,准确及时的故障检测是降低风电机组运行维护成本的关键。然而,现有检测方法未充分挖掘功能单元间的潜在时空关联,限制了检测准确性的提升。文中提出了一种基于多层级时空图神经网络的风电机组在线异常检测方法,以提高故障检测的准确性。该方法依据风电机组物理结构,将其功能单元划分为多个子图,从而构筑了一个多层级的时空图神经网络,通过图注意力机制和多头注意力机制全方位地分析风电机组各传感器节点与功能单元之间的关联强度。同时,针对数据采集与监控(SCADA)系统数据的时间关联,设计了动态图神经网络和时间注意力机制,使正常行为预测模型捕捉了SCADA系统数据的时间关联特性,实现了空间和时间特性的有效融合。最后,基于中国上海某风电场的实际数据验证了所提方法的显著有效性。

基于工况辨识的风电机组主传动系统运行状态监测

针对风电机组主传动系统运行工况复杂多变,导致状态监测系统误报警率高的问题,提出一种基于工况辨识的风电机组主传动系统运行状态监测方法。首先采用最大互信息系数(MIC)选取数据采集与监视控制系统(SCADA)特征参数,运用k-均值聚类算法对主传动系统运行工况进行划分,解决机组运行状态复杂多变导致运行工况不清的问题;接着提取状态监测系统(CMS)特征参数,并采用层次分析法计算CMS特征参量相对权重,提出主传动系统运行状态评价指标与量化算法;最后利用核密度估计(KDE)方法确定阈值,根据阈值与运行状态指标的关系实现主传动系统异常监测。将该方法运用于实际风场,结果表明所提方法监测结果与实际相符。

SCADA系统在能源供应系统中的应用研究

随着我国节能环保事业的发展和公用事业的进步,以及计算机技术、通信技术、互联网技术的成熟,数据采集与监视控制(SCADA)系统的应用获得了飞跃式发展。在能源供应领域,SCADA系统的应用从代替人工上门抄表、人工统计计算、人工现场巡查,发展到复杂的统计分析以及对能源供应系统进行全面的监控,在实施“双碳行动”中发挥着重要作用。首先,阐述了能源供应SCADA系统的基本功能。然后,列举了通过SCADA系统实现能源管网管理的增强功能和应用效果。最后,归纳总结了SCADA系统的发展趋势。SCADA系统在能源供应系统中的应用,对“双碳”目标的实现具有积极促进作用。

发动机装配生产线ANDON系统的关键技术实现

结合我国发动机装配生产线自身特点,在建立装配生产线的数据采集与监控系统的基础上,开发了发动机装配生产线ANDON系统。该系统实现了现场设备与车间人员的实时信息交互,从现场级层面保证了发动机的装配质量,具有重要的理论与实际应用价值。

基于聚合电网空间特征的电力线路参数辨识

电网线路参数辨识具有十分重要的意义,而电网结构日益复杂和数据污染等因素对参数辨识造成不良影响,传统的参数辨识方法已不能充分满足现有电力工作的需要。针对这一问题,文中提出了一种将图卷积运算和Transformer结合的多任务自注意力图卷积(SAGCN)模型,从不同的角度深度挖掘电网线路的空间特征。切比雪夫图卷积对空间特征信息进行预处理,Graph Former实现全局信息交互与邻接关系重构。通过对比传统算法和噪声干扰实验表明:所提出的模型不仅提高了多目标、多支路参数识别的精度,同时具有良好的鲁棒性。

激光跟踪仪CFIII三维控制网数据采集软件研制

根据激光跟踪仪自身特点和实际工程应用需要,设计开发了一款激光跟踪仪CFIII三维控制网数据采集软件,该软件主要为高速磁浮、火箭撬滑轨测设一类具有特殊精度要求的测量工作提供外业数据采集服务。本文首先按照软件开发的一般流程进行需求分析和功能设计,并以此为基础对激光跟踪仪控制网测量等相关功能算法进行研究,然后对其中一些关键的功能算法进行阐述,再对各项子功能进行编码实现。最后,本文设计了一系列测量对比分析和仿真计算实验对软件各项功能进行可靠性验证,实验结果表明,本文研制的软件运行正常,测量结果正确、可靠。

基于SCADA系统和信号处理的风力发电机主轴承故障分析与诊断

基于SCADA在线监测系统,通过该系统获取到主轴承实时振动数据,用小波包、集合经验模态分解模型进行信号处理和提取故障特征,分析其时域图的冲击信号、频谱图故障特征频率、包络谱故障频率幅值和谐波信号倍数情况,可以判断出主轴承故障位置以及受损情况,实现主轴承故障预警,为风力发电场现场运维人员高效维修提供帮助,降低运维成本。

基于QScript脚本的SCADA系统自定义报表方法研究

传统SCADA系统通常只提供固定报表模板,无法满足用户多样化需求。因此,提出并实现了一种基于QScript脚本的创新方法,以提高系统的灵活性和用户体验。首先,通过引入易于编写和跨平台的QScript脚本技术,实现了数据源的动态选择与处理。这使得用户可以根据实际需求动态地选择和处理数据,提高了系统的灵活性和数据处理能力;其次,实现了报表模板的灵活设计与编辑。通过QScript脚本,用户可以自由设计和编辑报表模板,使其更符合实际需求。此外,还提供了丰富的报表模板编辑工具和选项,使用户能够轻松创建和编辑自定义报表;最后,通过QScript脚本实现数据与报表的动态绑定与展示。这确保了数据的实时更新和报表的动态展示,进一步提高了系统的灵活性和用户体验。实验表明,提出的方法增强了SCADA系统的自定义报表生成能力,提高了灵活性,降低了操作复杂性和开发成本,为SCADA系统报表生成提供了有力支持,能满足用户多样化需求,具有广泛的应用前景。

基于SCADA告警数据的电网故障类型判断方法

为了提高电网故障诊断效果,提出基于SCADA告警数据的电网故障类型判断方法。构建引入动量项、自适应学习率的改进小波神经网络故障识别模型,采用提升小波对元器件两端线路数正序信号进行分裂、预估、调整等过程分解,获取不同尺度正序信号输入神经网络,输出结果即为细化的电网故障类型。实验结果表明,分解尺度为3时,故障录波信号均方误差最小,小波神经网络性能更稳定。该方法根据相间两相电流突变量情况判断故障类型及故障相,可精准判断电网故障类型及故障原因。

SCADA与DCS一体化监控技术要点分析

为深入分析监控与数据采集(SCADA)和分布式控制系统(DCS)一体化监控技术的要点,并探讨其在工业自动化领域的应用,本文首先设计了基于一体化监控技术的系统框架,该框架融合了SCADA和DCS的关键功能。然后详细讨论了其关键模块,包括数据采集与处理模块、数据分析与预测模块等。最后通过实验测试证明该系统的高效准确性。以期通过该论文研究成果为工业生产提供更高效、可靠和智能的监控方案,推动工业自动化向更高水平发展。

数字孪生系统中泵站调度方案自动执行软件的研究及应用

为了实现“预案”中的泵站调度方案在SCADA中自动执行,研发了决策方案自动执行软件,该软件主要由信息配置、指令解析、信息服务、指令执行4个模块组成。该软件可实现决策方案安全、自动执行,解决了水利工程数字孪生系统的“预案”中泵站调度方案自动执行问题,打通了数字孪生系统的“最后一公里”,对提升泵站智能化、自动化水平具有重大意义。

面向工程机械行业的SCADA系统的设计及实现

工程机械行业的企业生产车间大多涉及多个厂区,涵盖许多类型各异的生产机加设备,导致对设备复杂数据的监控、采集和分析等工作非常困难,同时与各个设备相关的设计、工艺、制造和售后等的复杂流程,也导致了管理上的混乱问题。因此,通过消除设计、工艺、制造和售后等各个环节的信息孤岛,统一监控和采集各个生产机加设备的数据并形成一体化的数字化管理模式,设计和开发一套智能化的SCADA系统是解决这类问题的关键举措。

基于Datawin组态软件的CG6000型直拉硅单晶炉数据采集系统

针对CG6000型直拉硅单晶炉数据运用效率低、难以实时存储和设备管理分散问题,设计应用串口通信的单晶炉数据采集系统,实现单晶拉制全工艺过程的实时数据采集。以SQL Server构建的单晶拉制数据库作为数据源,采用Datawin组态软件进行界面数据展示和开发。实际应用表明:该系统易于搭建、运行稳定,可以满足企业对单晶硅拉制参数的集中监控、存储、展示、分析及验证等多种实际需求。

Free Residual Chlorine Calibration by WaterCAD at El-Nozha Water Network in Alexandria Governorate, Egypt

Most of developing countries suffer from decreasing and poor quality of drinking water which led to emergence of many dangerous diseases. In addition, there isn’t any methodology followed to predict and disinfect drinking water by using advanced software program. Maintaining water quality in the water distribution system has become a prominent issue in the study of the water network. Residual chlorine concentration is the indicator to ensure the quality of water in the water network because it eliminates contaminants in the distribution network beginning of the treatment plant down to the consumer. In collaboration with Alexandria (Egypt) Water Company, samples were taken from El-NOZHA water plant station and El-HADARA water distribution network to know the free residual chlorine. In this paper, WaterCAD software has been used to make hydraulic analysis and calibration of residual chlorine in water distribution network to know the ideal chlorine dose that should be added at the water treatment plant and to know the areas of strength and weakness in the concentration of free residual chlorine in the water distribution network. In addition different scenarios have been found to know the free residual chlorine at the weakness areas after injecting chlorine in some junctions and the impact of a fire case or breaking in the water pipe distribution network on the residual chlorine. Results showed ensuring in the water quality in the distribution network by adding chlorine dose in water less than the existing dose which has been added in the El-NOZHA water treatment plant. It is possible to maintain the percentage of free residual chlorine concentration at different locations without relying on adding chlorine only in water treatment plant by injecting low percentage of chlorine dose in the junctions.

基于四分位与CFSFDP的风电机组异常数据清洗方法

在分析风机功率曲线异常数据的类型及产生原因的基础上,将异常数据划分为堆积型和离散型;在进行简单的异常数据剔除后,分别利用四分位–快速密度峰值聚类、快速密度峰值聚类–四分位这2种不同的组合方法进行数据清洗。将取自数据采集与监视控制系统的4台风机历史运行数据用于实验验证,并采用数据剔除率以及相关性指标来判断异常数据的清洗效果。实验结果表明所提算法可行、有效。

基于时空特征深度融合的双馈式风力发电机故障早期预测

风力发电机组监控与数据采集(SCADA)系统记录了机组运行参数,包含丰富的运行状态变化信息,为风力发电机组的健康状态识别提供了数据来源。提出一种基于自注意力机制级联的时空特征深度融合网络模型(SC-TSFN),模型采用空洞卷积层增大感受野,提取不同变量之间的空间特征,通过双向长短期记忆网络学习空间特征前后时间依赖性,自注意力机制突出关键变量空间特征权重以及动态调节局部与全局时间特征信息。对2.0 MW风电机组10 min间隔SCADA数据分析结果表明,该方法实现了风力发电机故障的早期预测并消除了误报。