Current situation and trend of marine data buoy and monitoring network technology of China

Marine data buoy can provide a long-term, continuous, real-time, reliable data of ocean observation in a variety of complex marine environment. It is one of the most reliable, most effective and important means of ocean monitoring technology. In this paper, the classification, main theory and technology system of marine data buoy are summarized. The typical technological breakthrough of the development of marine data buoy in recent years is summarized. The composition and application of marine monitoring network in China was introduced, and the gap between the technology of China＇s marine data buoy and the international advanced countries is compared.Combined on the situation and demand of China＇s current situation and needs, the development trend of marine data buoy and buoy monitoring network are expected.

Fault detection and diagnosis of permanent-magnetic DC motors based on current analysis and BP neural networks

In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper.

Backstepping sliding mode control with self recurrent wavelet neural network observer for a novel coaxial twelve-rotor UAV

The robust attitude control for a novel coaxial twelve-rotor UAV which has much greater payload capacity,higher drive capability and damage tolerance than a quad-rotor UAV is studied. Firstly,a dynamical and kinematical model for the coaxial twelve-rotor UAV is designed. Considering model uncertainties and external disturbances,a robust backstepping sliding mode control( BSMC) with self recurrent wavelet neural network( SRWNN) method is proposed as the attitude controller for the coaxial twelve-rotor. A combinative algorithm of backstepping control and sliding mode control has simplified design procedures with much stronger robustness benefiting from advantages of both controllers. SRWNN as the uncertainty observer is able to estimate the lumped uncertainties effectively.Then the uniformly ultimate stability of the twelve-rotor system is proved by Lyapunov stability theorem. Finally,the validity of the proposed robust control method adopted in the twelve-rotor UAV under model uncertainties and external disturbances are demonstrated via numerical simulations and twelve-rotor prototype experiments.

NOVEL REALIZATION OF FIRST-ORDER ALL-PASS NETWORKS USING CURRENT CONVEYER

Two novel networks for realizing first-order all-pass transfer functions are intro-duced. The networks use a current conveyer, a buffer and only three passive elements, and theyexhibit a high input impedance.

Neural network prediction of the shunt current in resistance spot welding

An error back propagation （BP） neural network prediction model was established for the shunt current compensation in series resistance spot welding. The input variables for the neural network consist of the resistivity of the material, the thickness of workpiece and the spot spacing, and the shunt rate is outputted. A simplified calculation for the shunt rate was presented based on the feature of the constant-current resistance spot welding and the variation of the resistance in resistance spot welding process, and then the data generated by simplified calculation were used to train and adjust the neural network model. The neural network model proposed was used to predict the shunt rate in the spot welding of 20# mlid steel （in Chinese classification） （in 2. 0 mm thickness） and 10# mild steel （in 1.5 mm and 1.0 mm thickness）. The maximum relative prediction errors are, respectively, 2. 83%, 1.77% and 3.67%. Shunt current compensation experiments were peoCormed based on the neural network prediction model proposed to check the diameter difference of nuggets. Experimental results show that maximum nugget diameter deviation is less than 4% for both 10# and 20# mlid steels with spot spacing of 30 mm and 50 mm.

Numeral eddy current sensor modelling based on genetic neural network

This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network （GNN） according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method.

Novel Minimum Passive Element Realization of All-Pass Network Using A Modified Current Conveyor

1. IntroductionA large number of networks for realizing first and second order transfer functions using a currentconveyor have been reported in the literature. Especially, the networks that can offer highinput impedance attract attention, for high input impedance has the advantage that the networksmay be used in cascade without requiring impedance matching device. In the Higashimura and

Current Efficiency of Low Temperature Aluminum Electrolysis Studied by Neural Network

A prediction model for Current Efficiency (CE) of low temperature aluminum electrolysis (LTAE) with the low molar ratioelectfolyte of Na3AIF6-AIF3 - CaF2-MgF2-LiF -Al2O3 system was investigated based on artificial neural network principles. The nonlinearmapping between CE of LATE and various electrolytic conditions was obtained from a number of experimental data and used to predictCE of LATE. The trsined neural networks possessed high precision and resulted in a good predicting effect. As a result, attificial neuralnetworks as a new cooperating and predicting technology provide a new approach to the further studies on low temperature aluminumelectrolysis.

A New Modeling Method Based on Genetic Neural Network for Numeral Eddy Current Sensor

In this paper,we present a method used to the numeral eddy current sensor modeling based on genetic neural network to settle its nonlinear problem.The principle and algorithms of genetic neural network are introduced.In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data.So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network.The nonlinear model has the advantages of strong robustness,on-line scaling and high precision.The maximum nonlinearity error can be reduced to 0.037% using GNN.However,the maximum nonlinearity error is 0.075% using least square method (LMS).

通信限制下多DG接入配网的新型电流差动保护

差动保护是解决分布式电源(distributed generation, DG)接入下配电网保护难题的有效手段,然而配网现有通信建设水平很大程度上制约了差动保护的构建。首先,融合电流相位与相量差动保护思想,同时将电流相位信息进行变换,实现通信信息降容。应用递推最小二乘法实现短路电流相位参数的快速估计,提出了短路电流相位修正策略,以应对CT饱和与直流分量对保护的干扰。最后,提出适用于含多DG配网的新型电流差动保护构建原则与保护判据。基于PSACD/EMTDC平台搭建仿真模型,验证了所提出的电流相位参数预测方法的精度与新型差动保护的性能。算例结果表明:所提电流相位参数预测方法速度快、精度高,具有良好的抗CT饱和能力;所提新型差动保护原理具有良好的性能。

采用电流增量关联度的柔直配电线路双端量保护

针对有效提升多端柔性直流配电线路双端量保护动作速度以及抗干扰能力的问题,提出一种基于故障电流增量关联度比较的直流线路双端量快速保护方法。该方法利用直流线路两侧故障电流增量的变化特性,利用灰色T型关联度算法对故障电流增量的变化态势实测数据求解关联度,分别对正负极线路首末端电流增量序列的灰色T型关联度进行比较,实现线路故障的快速切除。通过在PSCAD/EMTDC平台搭建±10 kV多端柔性直流配电系统进行算例仿真分析,结果表明该保护方法可以准确判别区内外故障,保护实现流程原理简单,动作快速可靠,具有良好的适用性。

基于频率切换实现恒流/恒压输出的电场耦合无线电能传输系统

电场耦合无线电能传输(electric-field coupled wireless power transfer,EC-WPT)技术的实际应用中,有些用电设备需要系统具有不同的恒定输出特性(恒流/恒压),即系统输出电压或输出电流与负载解耦,此外,系统还需具备在恒流、恒压模式间按需切换的功能。针对该需求,基于LC-CLC谐振网络提出1种具有恒流/恒压输出特性的EC-WPT系统,分析LC-CLC谐振网络特性,推导恒流/恒压输出特性的实现条件以及恒流频率和恒压频率的计算方法,并给出系统参数设计方法,分析系统对工作频率的敏感性。最后通过仿真和实验验证所提出的EC-WPT系统恒流/恒压输出特性及其参数设计方法的正确性和有效性。实验结果表明所提出的系统在输入电压恒定的不同负载工况下分别实现2 A的恒流输出以及96 V的恒压输出,其最大传输效率分别为87.83%及88.17%。

基于模型迁移和SVM的煤矿电网单相接地电容电流预测方法

针对智慧化矿山背景下煤矿电网单相接地电容电流预测精度低和智能预测实现困难的问题,提出了一种单相接地电容电流智能预测方法。采用模型迁移思想扩充矿用橡套软电缆的电容电流样本数据,以解决智能预测中电容电流数据不完备的问题;基于Sobol’敏感性方法分析了影响单相接地电容电流的关键因素及其各因素的交互关系,确定智能预测模型输入特征量;在此基础上,通过基于稀疏技术的支持向量机建立煤矿电网单相接地电容电流智能预测模型,并引入鲸鱼算法优化预测模型超参数,克服了电容电流数据样本容量小的不足。采用厂家测试数据及多家煤矿电网现场实测数据进行试验,结果表明:电缆绝缘层外径和绝缘层内径是影响单相接地电容电流的关键因素;所提方法预测煤矿电网单相接地电容电流的平均误差为2.26%,相较于现有预测方法,误差分别下降了34.19%,24.91%和7.40%。该方法实现了煤矿电网单相接地电容电流的准确预测,并为其智能化预测提供了新思路。

基于限流电抗器电压的直流配电网单端量保护

直流配电网故障特性复杂,故障电流上升速度快且峰值大,快速、灵敏的故障识别与线路保护是保障直流配电网安全运行的关键技术之一。为此,针对增设限流电抗器的直流配电网,提出一种利用限流电抗器电压实现高灵敏故障识别的直流配电网单端量保护方案。首先,通过分析含限流电抗器的直流配电网故障特性,明确区内、区外故障时限流电抗器电压的特征差异。其次,研究限流电抗器对故障特性及直流保护的影响机制,提出虚拟补偿策略以增强故障识别的灵敏性。在此基础上,根据线路电压变化率设计保护启动判据,提出基于虚拟补偿的故障识别判据和故障选极判据,并给出完整的保护方案流程。最后,基于PSCAD/EMTDC仿真平台进行大量的仿真测试,结果表明所提保护方案能够实现对直流故障的快速准确判断,无需通信支持,且对过渡电阻、噪声及负荷波动具有较强的耐受能力。

基于VMD模糊熵与GG聚类的直流配电网故障检测方法

针对直流配电网存在的故障信号难以提取、不易对各类故障进行诊断等问题,提出一种基于变分模态分解(VMD)模糊熵与Gath-Geva(GG)聚类的故障检测方法。首先,提取出暂态电流,采用VMD算法将故障暂态电流分解成若干个固有模态分量(IMF)。然后,分别计算分解得到的若干个IMF的模糊熵,将其作为特征向量。最后,采用GG聚类算法对故障特征的特征向量进行聚类识别。GG聚类的主要算法为将聚类样本划分为c类,设出隶属度矩阵,通过设定迭代来计算聚类中心与最大似然估计距离,更新隶属度矩阵,当隶属度矩阵满足条件矩阵时终止迭代,从而实现对单极故障、极间故障以及区外交流侧接地故障的聚类识别。仿真结果表明,所提保护方案可靠性强、准确率高,在不同故障类型、故障位置和过渡电阻等工况下均能可靠检测直流线路故障并准确识别故障类型,且具备一定的抗干扰能力。

脉冲晶闸管热网络模型及运行状态评估方法

针对脉冲晶闸管承受强流脉冲导致的瞬态电磁热力联合冲击产生的热疲劳累积突发性失效问题,提出了在脉冲晶闸管内布置温度传感器的状态监测方法,建立了脉冲晶闸管的高阶瞬态热阻抗网络模型,分析了强流脉冲作用下脉冲晶闸管内部不同层的温度分布规律,并通过理论计算验证了分析方法的有效性。计算结果表明,热网络模型计算相对偏差小于5%。可为脉冲晶闸管型强流开关的状态评估提供理论指导。

基于CNN-BiGRU的高压直流输电线路故障识别

针对高压直流(high voltage direct current,HVDC)输电线路故障暂态行波具有时序性和强非线性的特点,导致高过渡电阻情况下故障识别率低的问题,提出基于卷积神经网络(convolutional neural networks,CNN)和双向循环门单元(bidirectional gate recurrent unit,BiGRU)的HVDC输电线路故障识别方法。首先,采用故障后整流侧的双极暂态电流行波作为特征向量,利用CNN提取全局特征,并从中剔除噪声和不稳定成分,完成对数据的降维处理。然后,采用BiGRU来捕获CNN提取到特征的前后时间信息,进一步提取数据中的时序特征,以实现HVDC输电线路故障识别。仿真结果表明:该方法可在不同故障地点以及不同过渡电阻下对单极接地、双极短路、雷击故障、雷击干扰共四种故障实现准确识别,可靠性高,具有较强的耐受过渡电阻能力,同时具备一定的抗噪性能。

Statewide GNSS-RTN Systems: Current Practices

The applications of geospatial technologies and positioning data embrace every sphere of modern-day science and industry. With technological advancement, the demands for highly accurate positioning services in real-time led to the development of the Global Navigation Satellite System—Real-Time Network (GNSS-RTN). While there is numerous published information on the technical aspects of the GNSS-RTN technology, information on the best practices or guidelines in building, operating, and managing the GNSS-RTN networks is lacking in practice. To better understand the current practice in establishing and operating the GNSS-RTN systems, an online questionnaire survey was sent to the GNSS-RTN system owners/operators across the U.S. Additionally, a thorough review of available literature on business models and interviews with representatives of two major manufacturers/vendors of GNSS-RTN products and services were conducted. Study results revealed a great deal of inconsistency in current practices among states in the way the GNSS-RTN systems are built, operated, and managed. Aspects of the diversity in state practices involved the business models for the GNSS-RTN systems besides the technical attributes of the network and system products. The information gathered in this study is important in helping state agencies make informed decisions as they build, expand or manage their own GNSS-RTN systems.

小级差需求下基于故障首半波比较的快速电流保护

基于时间级差配合的梯级电流保护受限于出口线路保护动作时间,为缩短保护研判时间,提升配电网多级配合能力,提出一种小级差需求下基于故障首半波比较的快速电流保护方法。对传统电流保护动作时间进行分析,采用整定值构建模板曲线,与故障电流采样值进行多点动态比较,基于有效值爬坡效应及采样值越限效应构建新的保护启动判据、考虑保护算法延迟及干扰点的影响构建新的动作判据,综合加快电流保护判别速度。利用PSCAD/EMTDC软件仿真验证了所提方法在不同类型故障、不同故障时刻、负荷突增、干扰影响等工况下的有效性,结果表明所提方法的动作时间不超过10 ms,有利于小级差更加灵活和可靠的整定。