复合型人才“网电工”培训方法的探索

“网电工”是一种复合型人才,“网电工”兼具接触网专业、电力线路维修专业知识与技能,能够在接触网专业、电力线路维修专业维修两个领域大显身手。衡阳供电段茶陵南供电车间的接触网工、电力线路工缺员,但是接触网专业、电力线路维修年度工作量没有减少。面对现实存在的困难,衡阳供电段干部职工转变思维、开拓思路,建立机制、强化管理,开展“复合型人才-网电工”培养,以此提高劳动用工效率,缓解缺员压力。并对“网电工”培养进行有益的探索。

Application of novel physical picture based on artificial neural networks to predict microstructure evolution of Al-Zn-Mg-Cu alloy during solid solution process

The effects of the solid solution conditions on the microstructure and tensile properties of Al?Zn?Mg?Cu aluminum alloy were investigated by in-situ resistivity measurement, optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and tensile test. A radial basis function artificial neural network (RBF-ANN) model was developed for the analysis and prediction of the electrical resistivity of the tested alloy during the solid solution process. The results show that the model is capable of predicting the electrical resistivity with remarkable success. The correlation coefficient between the predicted results and experimental data is 0.9958 and the relative error is 0.33%. The predicted data were adopted to construct a novel physical picture which was defined as “solution resistivity map”. As revealed by the map, the optimum domain for the solid solution of the tested alloy is in the temperature range of 465?475 °C and solution time range of 50?60 min. In this domain, the solution of second particles and the recrystallization phenomenon will reach equilibrium.

Development of Network Interconnection in China

This paper introduces the current situation of China power industry and interconnection, the necessity to develop interconnection, the principle of nationwide interconnection and the key technologies to be studiedinclude HVDC and FACTS. The paper also discusses thefeasibility of 750 kV to be used in the northwest.regionand to speed up research and development of nighervoltage level in other regions of China, as well as scl-ence and technical innovation for transmission and dis-tribution projects.

PC-based artif icial neural network inversion for airborne time-domain electromagnetic data

Traditionally, airborne time-domain electromagnetic （ATEM） data are inverted to derive the earth model by iteration. However, the data are often highly correlated among channels and consequently cause ill-posed and over-determined problems in the inversion. The correlation complicates the mapping relation between the ATEM data and the earth parameters and thus increases the inversion complexity. To obviate this, we adopt principal component analysis to transform ATEM data into orthogonal principal components （PCs） to reduce the correlations and the data dimensionality and simultaneously suppress the unrelated noise. In this paper, we use an artificial neural network （ANN） to approach the PCs mapping relation with the earth model parameters, avoiding the calculation of Jacobian derivatives. The PC-based ANN algorithm is applied to synthetic data for layered models compared with data-based ANN for airborne time-domain electromagnetic inversion. The results demonstrate the PC-based ANN advantages of simpler network structure, less training steps, and better inversion results over data-based ANN, especially for contaminated data. Furthermore, the PC-based ANN algorithm effectiveness is examined by the inversion of the pseudo 2D model and comparison with data-based ANN and Zhody＇s methods. The results indicate that PC-based ANN inversion can achieve a better agreement with the true model and also proved that PC-based ANN is feasible to invert large ATEM datasets.

NEW PRINCIPLE FOR DIRECTIONAL COMPARISON CARRIER PROTECTION OF EHV TRANSMISSION LINES

The increasing scale and complexity of power systems require high performance and high reliability of power system protection.Protective relaying based on directional comparison with power line carrier or microwave channels is the most suitable protection scheme for long distance EHV transmission lines and is widely used in power systems.The key element of such protection is a directional relay used to discriminate the fault direction.In order to overcome the disadvantages of conventional directional relays,the authors of this paper put forward the directional comparison carrier protection based on the artificial neural network(ANN).The protection is extensively tested using electromagnetic transient program (EMTP) under various electric power system operating and fault conditions.It is proved that the directional comparison carrier protection based on ANN,which can recognize various fault patterns of the protected transmission line(such as fault direction,fault phases etc.)correctly in any kind of operating and fault conditions and the whole process,is satisfactory for EHV transmission line protection.

Design and Realization of CPW Circuits Using EC-ANN Models for CPW Discontinuities

Novel accurate and efficient equivalent circuit trained artificial neural-network （EC-ANN） models,which inherit and improve upon EC model and EM-ANN models＇ advantages,are developed for coplanar waveguide （CPW） discontinuities. Modeled discontinuities include ： CPW step, interdigital capacitor, symmetric cross junction, and spiral inductor, for which validation tests are performed. These models allow for circuit design, simulation, and optimization within a CAD simulator. Design and realization of a coplanar lumped element band pass filter on GaAs using the developed CPW EC-ANN models are demonstrated.

NETLAB-An Internet based laboratory for electrical engineering education

This article describes an Internet based laboratory (NETLAB) developed at Zhejiang University for electrical engi- neering education. A key feature of the project is the use of real experimental systems rather than simulation or virtual reality. NELTAB provides remote access to a wide variety of experiments, including not only basic electrical and electronic experiments but also many innovative control experiments. Students can effectively use the laboratory at any time and from anywhere. NETLAB has been in operation since July 2003.

Modeling the complex and long term swelling behavior of argillaceous rocks

The swelling behavior of argillaceous rocks is a complex phenomenon and has been determined using a lot of indexes in the literature. Determining the required modeling indexes that need to be performed requires expensive tests and extensive time in different laboratories. In some of the cases, it is too diffi- cult to find a relation between the effective variables and swelling potential. This paper suggests a method for modeling the time dependent swelling pressure of argillaceous rocks. The trend of short term swelling potential during the first 3 days of the swelling pressure testing is used for modeling the long term swelling pressure of mudstone that is recorded during months. The artificial neural network (ANN) as a power tool is used for modeling this nonlinear and complex behavior. This method enables predicting the swelling potential of argillaceous rocks when the required indexes and also correlation between them is unattainable. This method facilitates the model of all studied samples under a unique formulation.

Radio ＂Echo of Moscow＂ （Ekho Moskvy） as a Multimedia Project

The paper deals the popular news-talk radio station ＂Echo of Moscow＂ which is one of the most interesting and successful one in Moscow FM range. It provides thorough analysis of different previous and nowadays programs and web projects in comparison with main multimedia sphere trends. A particular attention is paid to ＂Echo of Moscow＂ web page as multimedia portal strongly packed with different functions such as its integration with social nets.

Personalization on the TV on Demand

The Interactive Intemet TV Project is intended to be a web tool for publishing, indexing and viewing videos; that offers ontology based recommendations. The application has been developed and tested as a platform for e-learning in a univerasity context and in Optiva Media Company. It combines expertise in education and television to develop a prototype,., where the contents are generated using an automatic recommendation model based on personal profiles of each user; and that can be accessed from different devices： computers, mobile phones and so on.

Application of fuzzy analytic hierarchy process and neural network in power transformer risk assessment

In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(FAHP) and artificial neural network(ANN) from the perspective of accuracy and quickness is proposed.An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix;with utilization of adaptive ability and nonlinear mapping ability of the ANN,the risk factors with large weights are used as input of neutral network,and thus intelligent quantitative assessment of transformer risk is realized.The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.

Research Reports on the 12^(th) Five-Year Plan for Electric Power Industry Power Grids

China Electricity Council organized competent authorities across the industry of electric power to work out the ＂Research Reports on the 12^th Five-Year Plan for Electric＂ Power lndustry ＂＂ in nearly one year, which provides a reference/or governmental departments to formulate the 12^th Five-Year Plan on energy and electric power industry. The magazine should publish the serial reports inchliding power sources, power grids, equipment manufacture, energy and environment, and power economics. This paper presents the part of ＂ power grids, ＂ in which the strategies of developing power grids are put forward.

A Method of Identifying Electromagnetic Radiation Sources by Using Support Vector Machines

Electromagnetic Radiation Source Identification(ERSI) is a key technology that is widely used in military and radiation management and in electromagnetic interference diagnostics.The discriminative capability of machine learning methods has recently been used for facilitating ERSI.This paper presents a new approach to improve ERSI by adopting support vector machines,which are proven to be effective tools in pattern classification and regression,on the basis of the spatial distribution of electromagnetic radiation sources.Spatial information is converted from 3D cubes to 1D vectors with subscripts as inputs in order to simplify the model.The model is trained with 187 500 data sets in order to enable it to identify the types of radiation source types with an accuracy of up to 99.9%.The influence of parameters(e.g.,penalty parameter,reflection and noise from the ambient environment,and the scaling method for the input data) are discussed.The proposed method has good performance in noisy and reverberant environment.It has an identification accuracy of 82.15% when the signal-to-noise ratio is 20 dB.The proposed method has better accuracy in a noisy environment than artificial neural networks.Given that each Electromagnetic(EM) source has unique spatial characteristics,this method can be used for EM source identification and EM interference diagnostics.

Incorporating Network Coding into TCP Grounded on Network Utility Maximization in Multi-Radio Multi-Channel Wireless Mesh Networks

A new approach, named TCP-I2NC, is proposed to improve the interaction between network coding and TCP and to maximize the network utility in interference-free multi-radio multi-channel wireless mesh networks. It is grounded on a Network Utility Maxmization (NUM) formulation which can be decomposed into a rate control problem and a packet scheduling problem. The solutions to these two problems perform resource allocation among different flows. Simulations demonstrate that TCP-I2NC results in a significant throughput gain and a small delay jitter. Network resource is fairly allocated via the solution to the NUM problem and the whole system also runs stably. Moreover, TCP-I2NC is compatible with traditional TCP variants.

Material Removal Rate Prediction of Electrical Discharge Machining Process Using Artificial Neural Network

This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data collected from an experimental of EDM process, whereas several research objectives have been outlined such as experimenting machining material for selected gap current, identifying machining parameters for ANN variables and selecting appropriate size of data selection. The experimental data （input variables） of copper-electrode and steel-workpiece is based on a selected gap current where pulse on time, pulse off time and sparking frequency have been chosen at optimum value of Material Removal Rate （MRR）. In this paper, the result has significantly demonstrated that the ANN model is capable of predicting the MRR with low percentage prediction error when compared with the experimental result.

Modelling and Analysis of Distribution Network with HTS Transformer

Transformers utilizing HTS （high temperature superconductors） are considered as a timely invention. The number of power transformers age more than 30 years old and nearing retirement is increasing. If this window of opportunity is not grabbed, there would be great reluctance to replace recently installed highly priced capital asset. Major projects of developing HTS transformers are well making progress in the United States, Europe, Japan, Korea and China which indicate the interest. The efforts must have been appropriately verified through the economic interest of the discounted losses. Consequently, it is very important to develop an understanding of the fundamental HTS transformer design issues that can provide guidance for developing practical devices of interest to the electric utility industry. The parameters of HTS transformer need to be validated before any effort is to carry out to model the behaviour of a distribution network under a range of conditions. The predicted performance and reliability of HTS transformers can then be verified through the network modelling and analysis calculation. The ultimate purpose is to furnish electric utilities precise information as to which HTS transformers work under various applications with greater technical efficiency and proven reliability.

Industrial Fault Signals Propagation and Current Signature Analysis

Motor current signature analysis provides good results in laboratory environment. In real life situation, electrical machines usually share voltage and current from common terminals and would easily influence each other. This will result in considerable amount of interferences among motors and doubt in identity of fault signals. Therefore, estimating the mutual influence of motors will help identifying the original signal from the environmental noise. This research aims at modelling the propagation of signals that are caused by faults of induction motors in power networks. Estimating the propagation pattern of fault signal leads to a method to discriminate and identify the original source of major events in industrial networks. Simulation results show that source of fault could be identified using this approach with a higher certainty than anticipated output coming of any individual diagnosis.

Discussion on the Application of Complex Power Network to Software Engineering

Along with the further development of science and technology, computer hardware and the Intemet are in a rapid development, and information technology has been widely used in all fields so that complex problems are simply solved. Because of the needs for the development, software starts to mutually integrate with complex power network, making the scale of software increase greatly. Such a growing trend of software promotes soft-ware development to go beyond a general understanding and control and thus a complex system is formed. It is necessary to strengthen the research of complex network theory, and this is a new way to help people study the complexity of software systems. In this paper, the development course of complex dynamic network is introduced simply and the use of complex power network in the software engineering is summarized. Hopefully, this paper can help the crossover study of complex power network and software engineering in the future.

Industrial Arrangement of Large-scale,Non-grid-connected Wind Power Industrial Zones in Coastal Areas of China

Constructing an industrial system for a large-scale,non-grid-connected wind power industry is a key step towards the diverse utilization of wind power.However,wind power exploitation is not only a technical challenge but an industrial problem as well.The objective of this study is to introduce a concept of large-scale,non-grid-connected wind power(LSNGCWP) industrial zones and establish an evaluation model to assess their industrial arrangement.The data of wind energy,industry,nature resources and socio-economy were collected in this study.Using spatial overlay analysis of geographic information system,this study proposes a spatial arrangement of the LSNGCWP indus-trial zones in the coastal areas of China,which could be summarized as the 'one line and three circles' structure,which will contribute to the optimization of the industrial structure,advance the wind power technology,coordinate the multi-industrial cooperation,and upgrade the industrial transformation of China's coastal areas.

Use of Artificial Neural Networks for Location of Defective Insulators in Power Lines

This is an extended version of the same titled paper presented at the 21st CIRED. It discusses a new technique for identification and location of defective insulator strings in power lines based on the analysis of high frequency signals generated by corona effect. Damaged insulator strings may lead to loss of insulation and hence to the corona effect, in other words, to partial discharges. These partial discharges can be detected by a system composed of a capacitive coupling device （region between the phase and the metal body of a current transformer）, a data acquisition board and a computer. Analyzing the waveform of these partial discharges through a neural network based software, it is possible to identify and locate the defective insulator string. This paper discusses how this software analysis works and why its technique is suitable for this application. Hence the results of key tests performed along the development are discussed, pointing out the main factors that affect their performance.