A Fault Diagnosis Method for Smart Meters via Two-layer Stacking Ensemble Optimization and Data Augmentation

The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power collectionsystems.However,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault categories.To address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct modules.The first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the classifiers.To enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse autoencoder.To tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation module.Finally,anovel Stacking ensemble classifier for SM fault diagnosis is developed.In contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM data.The proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,China.Experimental results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.

Intelligent Energy Utilization Analysis Using IUA-SMD Model Based Optimization Technique for Smart Metering Data

Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.

Functional Confirmation Using a Medical X-Ray System of a Semiconductor Survey Meter

In recent years, semiconductor survey meters have been developed and are in increasing demand worldwide. This study determined if it is possible to use the X-ray system installed in each medical facility to calculate the time constant of a semiconductor survey meter and confirm the meter’s function. An additional filter was attached to the medical X-ray system to satisfy the standards of N-60 to N-120, more copper plates were added as needed, and the first and second half-value layers were calculated to enable comparisons of the facility’s X-ray system quality with the N-60 to N-120 quality values. Next, we used a medical X-ray system to measure the leakage dose and calculate the time constant of the survey meter. The functionality of the meter was then checked and compared with the energy characteristics of the meter. The experimental results showed that it was possible to use a medical X-ray system to reproduce the N-60 to N-120 radiation quality values and to calculate the time constant from the measured results, assuming actual leakage dosimetry for that radiation quality. We also found that the calibration factor was equivalent to that of the energy characteristics of the survey meter.

Detection of Water Meter Digits Based on Improved Faster R-CNN

In order to more accurately detect the accuracy of word-wheel water meter digits, 2000 water meter pictures were produced, and an improved Faster-RCNN algorithm for detecting water meter digits was proposed. The improved Faster-RCNN algorithm uses ResNet50 combined with FPN (Feature Pyramid Network) structure instead of the original ResNet50 as the feature extraction network, which can enhance the accuracy of the model for small-sized digit recognition;the use of ROI Align instead of ROI Pooling can eliminate the error caused by the quantization process of the ROI Pooling twice, so that the candidate region is more accurately mapped to the feature map, and the accuracy of the model is further enhanced. The experiment proves that the improved Faster-RCNN algorithm can reach 91.8% recognition accuracy on the test set of homemade dataset, which meets the accuracy requirements of automatic meter reading technology for water meter digital recognition, which is of great significance for solving the problem of automatic meter reading of mechanical water meters and promoting the intelligent development of water meters.

Controlling Congestion in Al-Seeb Highway Using Ramp Metering (Case Study)

This paper represents a case study of traffic congestion within a section on Al Seeb Street highway due to the on-ramp merging of vehicles that causes a bottleneck in the mainline road. It studies the efficiency of installing ramp metering within a ramp within the selected study zone. This is done by simulating the collected data using Vissim software by drawing three one-hour-long scenarios;the first scenario reflects the data collected for 30 minutes duration and is used as a base scenario to draw the other two scenarios, which are reflected as factored-up scenarios to create a situation observed in the early morning in the study zone at 6:00-7:00 in which slowing down of speeds exist, and breakdown is raised in working days. The two factoring-up scenarios were as follows: one without ramp metering and the other without ramp metering. Each scenario was calibrated and run five times with random seeds, and then the average was considered. The simulation examines the ability of RM to smooth traffic in mainline and reduce queuing on on-ramp roads within the selected study zone by comparing the performance of the network for the scenarios and comparing them in terms of the overall delays, number of stops and the average speeds for the vehicles within the mainline. The results showed a good performance reflected by the scenario with ramp metering with a reduction of the overall delay, a decrease in stops number and an increase of the average speed were achieved. For the base scenario, a visualization (video extracted from Vissim software) was extracted, showing no need to install RM with an associated table showing a number of stops equal to zero with an average speed of 102.74 km/h and a total delay of 6045 seconds. For the second scenario with no RM, a visualization was extracted showing a slowing down of speeds for vehicles within the mainline while vehicles merging from the on-ramp and need to be controlled with a table showing a number of stops equal to 16 and an average speed equal to 58 km/h and a total delay of 916,874 seconds. For the third scenario with RM, a visualization was extracted showing good control of the second scenario with a table showing the number of stops equal to 6, an average speed equal to 61 km/h and a total delay equal to 484,466 seconds. Ten literatures in regard to this study have been reviewed. The data collected are quantitative, which are collected using an indirect manual counting method and then the data is used to feed the software for simulation.

Personal glucose meters coupled with signal amplification technologies for quantitative detection of non-glucose targets:Recent progress and challenges in food safety hazards analysis

Ensuring food safety is paramount worldwide.Developing effective detection methods to ensure food safety can be challenging owing to trace hazards,long detection time,and resource-poor sites,in addition to the matrix effects of food.Personal glucose meter(PGM),a classic point-of-care testing device,possesses unique application advantages,demonstrating promise in food safety.Currently,many studies have used PGM-based biosensors and signal amplification technologies to achieve sensitive and specific detection of food hazards.Signal amplification technologies have the potential to greatly improve the analytical performance and integration of PGMs with biosensors,which is crucial for solving the challenges associated with the use of PGMs for food safety analysis.This review introduces the basic detection principle of a PGM-based sensing strategy,which consists of three key factors:target recognition,signal transduction,and signal output.Representative studies of existing PGM-based sensing strategies combined with various signal amplification technologies(nanomaterial-loaded multienzyme labeling,nucleic acid reaction,DNAzyme catalysis,responsive nanomaterial encapsulation,and others)in the field of food safety detection are reviewed.Future perspectives and potential opportunities and challenges associated with PGMs in the field of food safety are discussed.Despite the need for complex sample preparation and the lack of standardization in the field,using PGMs in combination with signal amplification technology shows promise as a rapid and cost-effective method for food safety hazard analysis.

ColorMeter和VISIA-CR图像提取肤色的方法对比

目的:建立基于VISIA-CR图像提取中国人群面部肤色表型的方法,比较VISIA-CR图像提取的肤色参数与ColorMeter肤色仪测量值的一致性,为利用面部图像数据提取肤色表型建立参考方法。方法:基于机器学习模型自建算法自动批量提取VISIA-CR图像的肤色参数,并比较提取面积、位置和光源模式对结果的影响;随后利用256例健康人群面部VISIA-CR图像提取的肤色参数与ColorMeter肤色仪测量值进行了Pearson相关性分析。结果:VISIA-CR标准光和交叉偏振光两种模式下提取的肤色参数强相关(L^(*),R=0.86;a^(*),R=0.90;b^(*),R=0.85;P<0.05),相邻或对称位置下不同面积的肤色参数也强相关(R>0.82,P<0.05),提示光源模式、相邻或对称位置和测量面积对VISIA-CR图像提取结果的影响不大。VISIA-CR图像提取的肤色参数与ColorMeter测量值有相关性(P<0.05),其中交叉偏振光模式下,较大提取面积的肤色参数L^(*)值与仪器测量值强相关(R>0.73)。结论:该方法可从VISIA-CR图像中准确提取肤色参数且能较好解决肤色不均的测量偏差问题,交叉偏振光模式和较大提取面积下的肤色参数L^(*)值更接近ColorMeter仪器测量结果,推荐用于肤色表型的提取。

对有关电能计量若干问题的思考

文中基于论证和较全面分析,就不同场景下电能计量所关注的截然不同的问题、更好利用大数据去构建电能表检验新方法、动态电能计量研究面临新挑战、关注超谐波电能计量、不宜将电能计量装置与电能表简单划等号,以及测量准确度不同于测量精度等六个问题,阐述了作者的观点和认识,旨在对开展相关研究,以及更清楚理解相关测量方法及概念等起到促进作用。

Monitoring energy usage of heavy-haul iron ore trains with on-board energy meter for improving energy efficiency

Purpose-For billing purposes,heavy-haul locomotives in Sweden are equipped with on-board energy meters,which can record several parameters,e.g.,used energy,regenerated energy,speed and position.Since there is a strong demand for improving energy efficiency in Sweden,data from the energy meters can be used to obtain a better understanding of the detailed energy usage of heavy-haul trains and identify potential for future improvements.Design/methodology/approach-To monitor energy efficiency,the present study,therefore,develops key performance indicators(KPIs),which can be calculated with energy meter data to reflect the energy efficiency of heavy-haul trains in operation.Energy meter data of IORE class locomotives,hauling highly uniform 30-tonne axle load trains with 68 wagons,together with additional data sources,are analysed to identify significant parameters for describing driver influence on energy usage.Findings-Results show that driver behaviour varies significantly and has the single largest influence on energy usage.Furthermore,parametric studies are performed with help of simulation to identify the influence of different operational and rolling stock conditions,e.g.,axle loads and number of wagons,on energy usage.Originality/value-Based on the parametric studies,some operational parameters which have significant impact on energy efficiency are found and then the KPIs are derived.In the end,some possible measures for improving energy performance in heavy-haul operations are given.

Development of High Accurate Family-use Digital Refractometer Based on CMOS

This study aims to develop a low-cost refractometer for measuring the sucrose content of fruit juice,which is an important factor affecting human health.While laboratory-grade refractometers are expensive and unsuitable for personal use,existing low-cost commercial options lack stability and accuracy.To address this gap,we propose a refractometer that replaces the expensive CCD sensor and light source with a conventional LED and a reasonably priced CMOS sensor.By analyzing the output waveform pattern of the CMOS sensor,we achieve high precision with a personal-use-appropriate accuracy of 0.1%.We tested the proposed refractometer by conducting 100 repeated measurements on various fruit juice samples,and the results demonstrate its reliability and consistency.Running on a 48 MHz ARM processor,the algorithm can acquire data within 0.2 seconds.Our low-cost refractometer is suitable for personal health management and small-scale production,providing an affordable and reliable method for measuring sucrose concentration in fruit juice.It improves upon the existing low-cost options by offering better stability and accuracy.This accessible tool has potential applications in optimizing the sucrose content of fruit juice for better health and quality control.

水平圆盘式大豆单粒精密排种器的设计与试验

为了实现大豆单粒精密播种,设计了一种水平圆盘大豆单粒精密排种器,并对其工作原理进行了分析。依据大豆种子的物理参数,对排种盘的型孔尺寸、直径等参数进行了计算分析,确定型孔尺寸的长、宽和高分别为9、5、9mm。为了得到排种器的最佳性能,以排种器转速、大豆品种为试验因素,以排种合格指数、重播指数、漏播指数为试验指标,利用EDEM软件进行仿真试验,结果表明:当排种器最佳转速为40r/min时,排种合格率均大于96.4%,重播率小于2%,漏播率小于1.7%。在最佳参数下,进行台架试验验证,结果表明:当排种器转速为40r/min时,3个品种的大豆种子的排种合格率均大于96.5%,重播率小于2%,漏播率小于1.7%,能够满足大豆单粒精密播种的要求。

基于数值模拟和变形监测的副井稳定性评价

急倾斜矿体开采时一般将副井布置在矿体下盘不远处,以致副井附近矿体开采可能诱发副井井壁应力集中或损坏。为此,以湖北三鑫为例,利用FLAC3D仿真软件,探究沿走向或垂直走向布置采场引起的副井围岩应力、位移变化,并采用应变计、位移计监测井壁应力与位移变化,评价副井稳定性。结果表明:沿矿体走向布置采场对采场围岩、悬空底柱及开采段副井井壁的稳定都极为不利,沿走向或大宽度垂直走向布置采场都不会引起地表及开采标高段附近的副井发生明显位移,但都会引起井口以下约250 m内的井壁因变形而发生应力集中;必须垂直走向布置采场并缩小采场宽度;采取4 m×4 m进路上向胶结充填二步骤采矿后,除岩土分界面附近井壁偶尔因开快车而造成连续2次垂直位移、应力差分别超1.2 mm、1.4 MPa外,其他监测值变化基本可忽略;地表基岩沉降观测表明各点沉降值变化一般不超过3 mm。

气吸双行错置式玉米密植精量排种器设计与试验

针对大豆-玉米复合密植播种模式下传统气吸式排种器单行种盘高转速作业导致充种时间短、气流稳定性差,难以实现高速精量密植播种的问题,设计了一种气吸双行错置式玉米密植精量排种器,阐述了排种器结构与工作原理,对其工作过程及关键部件进行理论分析,构建充种和投种环节的种子力学模型,确定排种盘内外环型孔排布、投种轮、气室等关键结构参数,并开展单、双气道内负压分布、型孔内气流场特性分析,基于DEM-CFD耦合方法对排种器的排种过程进行仿真分析,以作业速度、气室结构和负压为试验因素,充种合格指数、重充指数和漏充指数为评价指标,优选出最优气室结构。通过台架试验开展不同气吸式排种器排种性能对比试验。试验结果表明,在作业速度为5~10 km/h的高速密植工况下,气吸双行错置式密植精量排种器排种合格指数均大于88.7%,且作业速度为10 km/h时,相较于常用单圆环气吸式排种器合格指数提高5.5个百分点,漏播指数降低5.6个百分点;田间试验结果表明,在作业速度为5 km/h下,播种合格指数为95.7%,重播指数为1.6%,漏播指数为2.8%。提出的气吸双行错置式玉米密植精量排种器在高速作业时拥有良好的排种性能,能够满足大豆-玉米高速精量密植播种要求。

美国加州车网互动政策、标准与市场机制最新进展及其启示

电动汽车与电网互动(简称“车网互动”)可以释放电动汽车作为灵活性资源的潜力,能够有效提升电网调峰能力、促进高比例可再生能源消纳、提升电动汽车用户经济收益,日益受到各方重视。美国加利福尼亚州(简称“加州”)近些年在推动电动汽车与电网互动发展方面做了大量工作,取得了重要进展。首先,文中介绍了加州在电动汽车与电网互动配套政策法规方面的最新动向;之后,从技术标准、市场机制两方面介绍了加州在电动汽车与电网互动相关基础工作方面的最新进展;最后,结合中国电动汽车与电网互动发展实际提出了有关建议。

勺轮式蚕豆排种器设计与排种性能研究

为提升蚕豆机械化播种率,提出一种结构简单的勺轮式蚕豆排种器,分析不同排种转速与种子类型对排种性能的影响。首先,介绍勺轮式排种器结构与工作原理,统计分析蚕豆种子外形尺寸并划分为三类(小粒、中粒、大粒),设计蚕豆种子排种勺结构,理论分析排种器取种过程、清种过程与传种过程;然后,利用EDEM软件仿真排种器工作性能,建立排种器与三类蚕豆种子的仿真模型;最后,以合格指数、重播指数、漏播指数为排种性能评价标准,开展台架试验,验证勺轮式蚕豆排种器的可行性。结果表明:仿真环境下,排种器内蚕豆种子实际充种线的水平倾角与排种转速呈正相关。排种器适宜排种转速为10 r/min,中粒种子排种性能指标最优,其播种合格指数为90.3%,重播指数为6.9%,漏播指数为2.8%。当排种转速为10 r/min时,仿真试验的综合排种性能为:合格指数89.5%,重播指数6.25%,漏播指数4.25%;台架试验的综合排种性能为:合格指数89.2%、重播指数7.1%、漏播指数3.7%,与仿真试验结果基本一致,满足蚕豆播种要求,验证勺轮式蚕豆排种器的可行性。

玉米免耕播种机振动特性及对排种器排种性能的影响

为研究玉米免耕播种机振动特性以及振动对排种器漏播指数和重播指数的影响,搭建一套由振动加速度传感器、电荷放大器、USB采集卡组成的播种作业振动测试系统,在玉米免耕播种机免耕地表作业时进行振动测试采集播种作业的振动信号。因田间工作环境复杂,使用经典滤波法中的IIR滤波器滤除所采集振动信号中其他高频干扰信号,对田间采集振动信号进行时域分析,均方值作为时域分析指标值。结果表明:振动加速度均方根随着作业速度和旋耕机转速的增加呈线性增加,旋耕机转速和作业速度是引起玉米免耕播种机振动的主要因素,影响顺序为:旋耕机转速>作业速度。使用二次积分法对田间采集信号进行分析,得出播种机作业时最大振动位移为16.004mm,对振动信号进行频域分析,使用直接法求出功率谱密度。结果表明:玉米免耕播种机振动频率主要在0~100Hz之间,频率分布与旋耕机转速影响较大。根据田间振动信号所得工作参数搭建振动试验台,以播种机振动频率、振动幅值、作业速度为试验因素,合格指数、漏播指数为评价指标进行3因素3水平响应面试验。结果表明:作业速度、振动幅值和振动频率对合格指数和漏播指数影响较为显著。各因素对合格指数影响顺序为:振动幅值、作业速度、振动频率;各因素对漏播指数影响顺序为:振动幅值、振动频率、作业速度。研究结果可为降低玉米免耕播种机振动和优化指夹式排种器提供理论参考。

面向多目标优化主动运维的电能表远程在线评价系统设计

针对传统电能表远程在线评价系统中存在的系统建造成本耗费较大、评价结构过于繁琐等问题,从评价系统的构建和评价算法两方面开展研究。在评价系统的设计方面,加入反馈,使得该评价系统不仅可以整合各项评价指标进行规律化的采集,还可以对评价指标进行实时反馈,提高了评价的效率和评价信息传输的效率。在评价算法方面,基于解耦评价算法对电能表的运行状态和采集数据进行评价,对数据信息进行归纳处理,并根据大量的数据样本进行条理化分析评价。通过仿真实验可知,硬件反馈线路的数据传输效率高达98.6%,具有很好的传输能力。

国家天然气(页岩气)千亿立方米级产能基地共建共享模式

国家天然(页岩气)千亿立方米级产能基地建设是保障国家能源安全、支撑区域社会经济加快发展的重大战略。在借鉴油气共建共享成功经验基础上,构建了国家天然气(页岩气)千亿立方米级产能基地共建共享模式,提出了模式实施路径。研究结果表明:①国家天然气(页岩气)千亿立方米级产能基地建设涉及庞大的产业体系与多元主体不同利益诉求,需要以共建共享理念和系统工程思维为指引,立足多元主体与多维度要素协同构建共建共享模式。②模式实施五大路径:强化共建共享顶层战略设计,丰富发展政企共建共享方式,完善企业之间共建共享机制,畅通多元主体沟通协商渠道,健全共建共享应急预警体系。结论认为,该模式以战略视域统筹产能基地建设,能够实现对多元主体利益的有效协同,促进天然气(页岩气)资源开发利用与产业链价值提升,为带动区域社会经济发展并推进共同富裕提供有力支持。

氡计量标准器量值传递技术研究与应用

AlphaGUARD测氡仪被广泛用作氡标准仪器开展量值传递。本文通过标准氡室以及循环氡源验证AlphaGUARD测氡仪在低量程的准确性、高量程的一致性,并建立一定浓度范围(0.6~142.4 Bq·L^(-1))内的量值传递线性关系。为保证量值传递的可靠性,基于溯源到参考标准的氡标准仪器,将量值传递到循环氡源,分别开展以循环氡源以及标准仪器和一定浓度的水样(>5 Bq·L^(-1))作为参考标准的量值传递实验。本研究针对地下流体氡观测网内近年应用的4种新型测氡仪,制定应用氡标准仪器进行量值传递的7种技术方案,并在全国40多个氡观测站应用,其中有12个连续观测站应用2年,结果表明,校准系数相对偏差均小于5%,氡量值传递技术方案是可行的,可满足现有氡观测的校准需求。此外,还开展了传递方案的不确定度评价实验,以DDL型测氡仪为例,其最小不确定度可达5.5%(包含因子k=2)。本研究建立的新型测氡仪氡计量标准器量值传递技术方案,该方案科学可行,应用方案可有效保证新型测氡仪测值的稳定可靠。

气吸式杂交稻单粒排种器研制

针对气力式水稻精量排种器充种不稳定、单粒播种精度不高和播种量大的问题,该研究设计了一种具有矩形吸种孔和辅助充种装置的气吸式杂交稻单粒排种器。根据“吉田优”型杂交稻的长短轴重力分布情况,确定排种盘吸种孔形状;基于CFD-DEM(Computational fluid dynamics,Discrete element method)流固耦合理论,以吸附力为指标,进行5类具有相同面积的吸种孔单因素仿真试验,确定吸附力最大的吸种孔规格为0.8 mm×2.25 mm;以该型吸种孔为基础,选取辅助充种角、工作转速和工作负压为试验因素,以单粒率S、多粒率M和漏播率L为试验指标,开展Box-Bhnken台架试验,对试验结果进行响应曲面分析和多目标优化,得到排种盘辅助充种角为80.90°、工作转速为42.65 r/min、工作负压为621 Pa时,排种器的单粒率为86.91%,漏播率为3.63%。验证试验结果的排种器单粒率为86.36%、漏播率为3.41%,与优化结果吻合。研究结果可为后续气吸式杂交稻单粒排种器的优化设计和直播机整机作业精度的提高提供指导。