NEURAL NETWORK INTELLIGENT SYSTEM FOR THE ON-LINE OPTIMIZATION IN CHEMICAL PLANTS

A strategy of developing on-line optimization intelligent systems based on combiningflowsheeting simulation and optimization package with artificial neural networks(ANN)is presented inthis paper.A number of optimization cases for a certain chemical plant are obtained off-line byusing PROCESS-Ⅱ or other flowsheeting programming with optimization.Then,taking these cases astraining examples,we establish a neural network systems which can be used on-line as an optimizer toobtain setpoints from input data sampled from distributed control system through gross error detectionand data reconciliation procedures.Such an on-line optimizer possesses two advantages over nonlinearprogramming package:first of all,there is no convergence problem for the trained ANN to be usedonline;secondly,the frequency for setpoints updating is not limited because only algebraic calculationrather than optimization is required to be carried out on-line.Here two key problems ofimplementing ANN approaches to the on-line optimization

Transient Fault Locating Method Based on Line Voltage and Zero-mode Current in Non-solidly Earthed Network

为解决配电网小电流接地故障时故障点定位困难的问题，提出一种基于线电压和零模电流暂态分量的故障定位新方法。该方法借助馈线自动化（feeder automation，FA）系统实现，不需要额外增加设备。馈线终端检测线电压和零模电流信号，利用暂态线电压的希尔伯特变换与暂态零模电流的乘积计算故障方向参数，FA主站根据故障点前后方向参数极性相反的特征确定故障点所在的线路区段。介绍基于线电压的小电流接地故障检测和接地故障相确定的算法。数字仿真与试验结果表明该方法是正确可行的。

Power Line Monitoring Data Transmission Using Wireless Sensor Network

The WSN used in power line monitoring is long chain structure, and the bottleneck near the Sink node is more obvious. In view of this, A Sink nodes’ cooperation mechanism is presented. The Sink nodes from different WSNs are adjacently deployed. Adopting multimode and spatial multiplexing network technology, the network is constructed into multi-mode-level to achieve different levels of data streaming. The network loads are shunted and the network resources are rationally utilized. Through the multi-sink nodes cooperation, the bottlenecks at the Sink node and its near several jump nodes are solved and process the competition of communication between nodes by channel adjustment. Finally, the paper analyzed the method and provided simulation experiment results. Simulation results show that the method can solve the funnel effect of the sink node, and get a good QoS.

ON-LINE STATE PREDICTION OF ENGINES BASED ON FLAT NEURAL NETWORK

A flat neural network is designed for the on line state prediction of engine. To reduce the computational cost of weight matrix, a fast recursive algorithm is derived according to the pseudoinverse formula of a partition matrix. Furthermore, the forgetting factor approach is introduced to improve predictive accuracy and robustness of the model. The experiment results indicate that the improved neural network is of good accuracy and strong robustness in prediction, and can apply for the on line prediction of nonlinear multi input multi output systems like vehicle engines.

ANALYSIS OF DYNAMIC CHARACTERISTICS FOR RCG LINES USING THEORY OF LADDER NETWORKS

The theory of RC uniform ladder networks based upon the recurrence of voltage and cur-rent functions is extended as a vehicle to analyse the dynamic characteristics of reg lines. Meth-ods for computing the time constants and simplifying the transfer functions for reg lines are alsopresented.

Hybrid Neural Network Architecture for On-Line Learning

Approaches to machine intelligence based on brain models use neural networks for generalization but they do so as signal processing black boxes. In reality, the brain consists of many modules that operate in parallel at different levels. In this paper we propose a more realistic biologically inspired hybrid neural network architecture that uses two kinds of neural networks simultaneously to consider short-term and long-term characteristics of the signal. The first of these networks quickly adapts to new modes of operation whereas the second one provides more accurate learning within a specific mode. We call these networks the surfacing and deep learning agents and show that this hybrid architecture performs complementary functions that improve the overall learning. The performance of the hybrid architecture has been compared with that of back-propagation perceptrons and the CC and FC networks for chaotic time-series prediction, the CATS benchmark test, and smooth function approximation. It is shown that the proposed architecture provides a superior performance based on the RMS error criterion.

Fault Line Selection Method Considering Grounding Fault Angle for Distribution Network

In the distribution network system with its neutral point grounding via arc suppression coil, when single-phase grounding fault occurred near zero-crossing point of the phase voltage, the inaccuracy of the line selection always existed in existing methods. According to the characteristics that transient current was different between the fault feeder and other faultless feeders, wavelet transformation was performed on data of the transient current within a power frequency cycle after the fault occurred. Based on different fault angles, wavelet energy in corresponding frequency band was chosen to compare. The result was that wavelet energy in fault feeder was the largest of all, and it was larger than sum of those in other faultless feeders, when the bus broke down, the disparity between each wavelet energy was not significant. Fault line could be selected out by the criterion above. The results of MATLAB/simulink simulation experiment indicated that this method had anti-interference capacity and was feasible.

On-line Condition Monitoring and Diagnostic Network System for Rotating Machine Set

Several industrial computers and a server are combined to set up the on-line monitoring and diagnostic system of turbo-generator sets. The main function of the system is to monitor machine sets' running condition. Through analyzing running data, technicians can detect whether there exist faults and where they occur. To share and transmit the dynamic information of the turbo-generator sets, a distributed network system is introduced. NetWare network operating system is used in the LAN (Local Area Network) system. The LAN is extended to realize the sharing of data and remote transmission of information. Furthermore, functions of monitoring and diagnostic clients are listed.

Power Line Communications Networking Method Based on Hybrid Ant Colony and Genetic Algorithm

When solving the routing problem with traditional ant colony algorithm, there is scarce in initialize pheromone and a slow convergence and stagnation for the complex network topology and the time-varying characteristics of channel in power line carrier communication of low voltage distribution grid. The algorithm is easy to fall into premature and local optimization. Proposed an automatic network algorithm based on improved transmission delay and the load factor as the evaluation factors. With the requirements of QoS, a logical topology of power line communication network is established. By the experiment of MATLAB simulation, verify that the improved Dynamic hybrid ant colony genetic algorithm (DH_ACGA) algorithm has improved the communication performance, which solved the QoS routing problems of power communication to some extent.

Smart Home Networking: Lessons from Combining Wireless and Powerline Networking

Integrating the power grid technology with renewable power generation technologies, Demand Response (DR) programs enabled by the Advanced Metering Infrastructure (AMI) were introduced into the power grid in the interest of both utilities and residents. They help to achieve load balance and increase the grid reliability by encouraging residents to reduce their power usage during peak load periods in return for incentives. To automate this process, appliances, in-house sensors, and the AMI controller need to be networked together. In this paper, we compare mainstream network technologies applicable to home appliance control and propose a solution combining Power Line Communication (PLC) with wireless communication in smart homes for the purpose of energy saving. We extended NS-2, a popular network simulator, to model such combined network scenarios. Using a number of different routing strategies, we then model and evaluate the network performance of DR programs in smart homes in such a combined network.

一种适用于混合三端直流输电线路的故障定位方法

针对因结构复杂导致的混合三端直流输电线路故障定位困难的问题,提出了一种结合变分模态分解算法与改进卷积神经网络(CNN)的故障定位方法(VMD-CNN)。首先,利用PSCAD/EMTDC软件构建混合三端直流输电系统模型,获得故障电流数据,应用克拉克变换对其解耦,获取故障电流的线模分量;其次,对得到的线模分量进行变分模态分解(VMD),得到多个本征模态函数(IMF)分量,选取特征信息最丰富的IMF分量作为VMD-CNN模型的输入;然后,利用高效的分类模型支持向量机(SVM)判别故障发生的区域,将提取到的IMF分量作为SVM输入进行训练学习,可以准确判断出故障发生区域;最后,搭建VMD-CNN模型进行故障定位,挖掘出行波信号中蕴藏的故障信息,同时通过麻雀搜索算法优化CNN中的超参数,实现混合三端直流输电线路的精确定位。仿真结果表明:过渡电阻为100Ω,不同故障位置情况下的定位相对误差均在0.17%以内;故障位置为460 km,不同过渡电阻情况下的定位相对误差均在0.25%以内;过渡电阻为50Ω,不同故障类型情况下的相对误差均在0.3%以内。所提方法能够提升不同故障位置、过渡电阻和故障类型下的定位准确性。

A study on the Popular Words on Line——Take the Popular Words of Recent Years as Examples

Popular word on line is a kind of language form which is welcomed by netizens.The popular word on line is the most active part of network language.They are made of expressions what the internet users are established by usage.The popular words on line have a very strong nature of medium.They are the comprehensive products which come from the facts of social politics,economics,culture,environment and people's psychological activities etc.of a certain time.The total amount of new emerging popular words on line is not so many every year,but the influence of them can not be belittled.This study focuses on the popular words on line with the cases of the popular words occured in recent years.It works on the reasons for their rapid spreading.The author summarizes their characteristics and put forward some ways for the guidance of the popular words on line.

Contributing Factors for Delays during the Morning Commute Hours and the Impact of the Spread of COVID-19 for Metropolitan Train Lines in Japan

The present study aims to conduct 2 types of statistical analysis to reveal the impact of the spread of COVID-19 on train delays by comparing the potential contributing factors before, during and after the outbreak of the virus in the metropolitan train lines in Japan. First of all, the result of the present study clearly revealed the changes in contributing factors for train delays caused by the spread of COVID-19. Specifically, the contributing factors for train delays changed due to the decrease of passengers by the effect of the outbreak of the virus. Additionally, though large terminal stations were considered to be a major contributing factor in causing and increasing train delays in the past, this was not the case after the spread of COVID-19. Therefore, under such conditions, it is more effective to make improvements in small to medium stations and tracks rather than terminal stations. Furthermore, as the decrease in passengers also decreased train delays in commuter lines going to the suburbs due to the spread of COVID-19, the contributing factor for such lines is the excessive number of passengers. Therefore, as for countermeasures for train delays after the effects of COVID-19, it is necessary to disperse passengers in order to avoid passengers concentrating in the same time zones and train lines.

Real-time Performance Evaluation of Line Topology Switched Ethernet

Recently,switched Ethernet has become an active area of research because of its wide uses in industry.However,its uses have various real-time constraints on data communications.This paper analyzes the performance of the line topology switched Ethernet as a data acquisition network.Network calculus theory,which has been successfully applied to assess the real-time performance of packet-switched networks,is used to analyze the networks.To properly describe the activity of switches,a novel approach of modeling data flows into or out of switches is addressed.Based on our model,a concisely analytical expression of the maximal end-to-end delay in line topology switched Ethernet is derived.Finally,the relative simulation results are demonstrated.These results agree well with the analytical results,and thus they validate the data flow modeling techniques.

A Mobile Localization Strategy for Wireless Sensor Network in NLOS Conditions

The problem of mobile localization for wireless sensor network has attracted considerable attention in recent years. The localization accuracy will drastically grade in non-line of sight(NLOS) conditions. In this paper, we propose a mobile localization strategy based on Kalman filter. The key technologies for the proposed method are the NLOS identification and mitigation. The proposed method does not need the prior knowledge of the NLOS error and it is independent of the physical measurement ways. Simulation results show that the proposed method owns the higher localization accuracy when compared with other methods.

One high-efficiency analysis method for high-speed circuit networks containing distributed parameter elements

In the field of high-speed circuits, the analysis of mixed circuit networks containing both distributed parameter elements and lumped parameter elements becomes ever important. This paper presents a new method for analyzing mixed circuit networks. It adds transmission line end currents to the circuit variables of the classical modified nodal approach and can be applied directly to the mixed circuit networks. We also introduce a frequency-domain technique without requiring decoupling for multiconductor transmission lines. The two methods are combined together to efficiently analyze high-speed circuit networks containing uniform,nonuniform,and frequency-dependent transmission lines. Numerical experiment is presented and the results are compared with that computed by PSPICE.

Avian-power line interactions in the Gobi Desert of Mongolia:are mitigation actions effective?

Background:Electrocution and collisions on power lines are among the leading causes of non-natural mortality for birds.Power lines are exponentially increasing,particularly in developing countries,but mitigation strategies to prevent bird mortality are questionable.Mongolia combines a recently increased power line network,an abundant raptor population,a dangerous crossarm configuration and a habitat with no natural perches,producing many bird-power line interactions.Our aim is to assess the bird mortality caused by power lines in the Gobi Desert of Mongolia,to determine the factors increasing the risk of bird electrocution,and to evaluate the effectiveness of used retrofitting measures.Methods:In July 2019 we covered 132.9 km of 15 kV power lines checking 1092 poles.We also conducted bird tran-sects to record raptor and corvid richness and abundance,to assess species vulnerability to electrocution.Results:We recorded 76 electrocuted birds of 7 species.Electrocution rate was 6.96 birds/100 poles.The most affected species were Common Raven(Corvus corax)and Upland Buzzard(Buteo hemilasius),highlighting the electro-cution of 5 endangered Saker Falcons(Falco cherrug).By contrast,we only recorded 8 individuals of 5 species collid-ing with wires,the most affected being Pallas’s Sandgrouse(Syrrhaptes paradoxus).About 76.1%of sampled poles had some mitigation measure.Of these,96.6%were brush perch deflectors and 3.4%rotating-mirrors perch deter-rents.We found differences in electrocution rates among crossarm configurations,with the strain insulator with one jumper being the most lethal.Additionally,we found no correlation between bird abundance and electrocution rates,suggesting that some species are more sensitive to electrocution.Although no differences in total bird electrocu-tion rates were detected between poles with and without perch deterrents,when bird size is considered,deterrents reduced the mortality rate of small birds,while they were ineffective for medium-sized birds.Conclusions:Despite the widespread use of perch deterrents in the Mongolian power line network,there is still an alarming electrocution rate.This strategy is ineffective and some mechanisms,such as brush perch deflectors,may increase the electrocution rate for some medium-sized birds.Finally,we propose strategies to minimize the avian electrocution rate in the Gobi Desert.

A Review: Artificial Neural Networks as Tool for Control Food Industry Process

In the last year, interest in using Artificial Neural networks as a modeling tool in food technology is increasing because they have found extensive utilization in solving many complex real world problems. Due to this and as previous step at development of some project, this paper intends to introduce the reader inside neural networks: general characteristics of the ANN, their architectures, their rules of learning, types of networks and ANN’s create process. Also this paper presents a comprehensive review of food industrial applications of artificial neural networks in the last year. ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers with except the applications in the olive oil industry that are described with special emphasis.

FDTD Analysis of Nonuniform Lossy Cascade Multiconductor Transmission Lines

A hybrid algorithm is presented for nonuniform lossy multiconductor transmission lines (MTL) connected by arbitrary linear load networks. The networks are characterized by a state-variable equation which allows a general characterization of dynamic elements in the cascade networks. The method is achieved by the finite difference-time domain (FDTD) algorithm for the MTL, and the skin effect is taken into account, the more accurate method is used to compute the skin effect. And this method is combined with the computation of the nonuniform transmission lines. Finally, several numerical examples are given, these results indicate that: the current of the lossy MTL is smaller than the lossless of the MTL; and when the load networks contain the dynamic element, the transition time of the current is longer than the MTL connected by resistance only.

基于信号图像化和CNN-ResNet的配电网单相接地故障选线方法

配电网发生单相接地故障时,零序电流呈现较强的非线性与非平稳性,故障选线较为困难,针对此问题,提出一种基于信号图像化和卷积神经网络-残差网络的配电网单相接地故障选线方法。首先,利用排列熵优化变分模态分解算法的参数,将零序电流信号分解成一系列固有模态函数;其次,引入新的数据预处理方式,将固有模态函数转成二维图像,获得零序电流信号的时频特征图;最后,利用一维卷积神经网络提取零序电流信号的相关性和特征,利用残差网络提取时频特征图的特征,将两个网络融合,构建混合卷积神经网络结构,实现故障选线。仿真与实验结果表明,该方法能够在高阻接地、采样时间不同步、强噪声等情况下准确地选择出故障线路,可满足配电网对故障选线准确性和可靠性的需求。