Hui Hou

Survey on Application of Wireless Sensor Network in Smart grid

Abstract The Wireless Sensor Network (WSN) is a promoted technology applied in the communication system of smart grid, which enjoys low cost, low power dissipation, self-organization, and strong flexibility. This paper presents the applications of WSN in condition-based maintenance, smart metering, smart-home, fault location, distributed bus protection of power networks, as well as some other application in disaster prevention with the main technical characteristics of WSN. It not only proposes the basic design philosophy of WSNs applications in areas, but also summarizes the superiority of the communication systems using WSN in smart grid.

Study on Risk Assessment of the Electric Power Tower and Pole Damage in Power System Under Typhoon Disaster

Abstract For a long time, the risk assessment of the power system pays more attention to the risk of chain failure caused by the internal stability damage, but less on the natural disasters like typhoon. This paper chooses the damage probability of the electric power tower and pole that is easily affected by the typhoon disaster as the probability risk indicator. Through integrating the typhoon information, electric tower and pole information and micro-topography information, this paper explores the association between various factors and the electric power tower and pole damage by making full use of historical data and establishes the Logistic model of the electric power tower and pole damage probability under typhoon disaster. Based on typhoon “Mujigae”, this paper calculates the damage probability and magnitude of the 220kV electric power tower and pole in Zhanjiang, China. The result proves that the risk indicators and calculation methods proposed in this paper are scientific and reasonable

Energy internet risk assessment framework

Energy internet is a deep fusion product made up of electric power system, electrification traffic system and gas system linked by information technology. Using more complex technologies and covers larger geographical area as well as more diversity of users, it may be exposed to greater potential risks. The paper introduces the recent development status of energy internet globally. It proposes the typical energy internet structure and then set up a new framework for the risk assessment of energy internet. It analyzes the differences of risk assessment between energy internet and smart grid. It is a complicated system project for the future energy internet risk assessment research work. As it is still in the initial stage, there remains a lot of research work to be further developed.

ZigBee Technology Application in Wireless Communication Mesh Network of Ice Disaster

Abstract The coating ice of transmission lines will threaten the safety of electric power system seriously. In order to obtain the real-time ice-coating data of the transmission lines in ice disaster accurately, this paper designs a real-time monitoring system applied in transmission lines based on ZigBee technology and mesh topology architectures. This system takes advantage of low power consumption of ZigBee technology, self-organization and self-healing of mesh network, and uses the F-AODVjr(F-Ad hoc On-Demand Distance Vector Routing) to establish a ZigBee wireless communication mesh network. A simulating demonstration is given in the end with the discussing of the systems feasibility and practicability.

Review on Multi-objective Joint Economic Dispatching of Microgrid in Power System

Abstract As the global energy crisis and environmental pollution problems are becoming more and more serious, the microgrid (MG), as an effective supplementary means of large power grid, has gradually become the focus of attention. With the gradual maturity of MG technology and deepen practice of engineering application, the economic dispatching of MG has become the key issue in the field of MGs. This paper analyzes the status quo of domestic and international researches on MG economic dispatch from multiple perspectives, such as dynamic and static economic dispatch, demand side response, energy management and multi-MGs joint dispatch. It points out that the economic dispatching of micro-grid still has some problems to be further studied. For instance, the imperfect of uncertainty researches on renewable energy (RE) and load demand (LD) and the immaturity of research on MG economic dispatch. Finally, a summary and prospect for economic dispatch research is concluded.

Risk Assessment of Switchgears in Distribution System Considering Environmental Dependency

Abstract As a link between power system and users, the distribution network plays an important role. Therefore, the power quality and reliability of distribution system are of main significance to both users and power system. The switchgears are one of the most widely used components in the distribution network. Consequently, it is necessary to study the reliability of the distribution switchgears. Traditional researches on risk assessment of switchgears hardly considered the potential threat caused by high temperature, high pressure and humid environment, which is quite common in some areas especially the coastal areas of distribution network. In order to forecast the failure rates of switchgears better, the method for failure rate forecasting considering the environmental factors based on Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) are advanced in this paper. The related ambient temperature, humidity and the historical failure rate which were obtained from the actual operation distribution data from China Southern Power Grid (CSPG) are normalized firstly. And then create the training function to train the processed data. Next the BPNN and SVR are used to minimize the error between the predicted value and the actual value through iteration. A demonstration based on a coastal city in China is given to prove the validity of the algorithm. The environmental dependency result is compared with the result from the method that only the historical failure rate considered. A general conclusion can be drawn that the SVR method is more accurate than the BPNN method. And it is expected to be widely used in the whole CSPG in the future.

The Optimal Charging Strategy of EV Rational User Based on TOU Power Price

This paper deals with the optimal charging strategy of electrical vehicle (EV) rational user with different time and mileages based on time-of-use (TOU) power price. Four types of EV response model are established and the optimal charging strategy of rational user is given based on five period of charging price and three period of discharging price. Under the price, the paper calculates charging, load and network loss. The power load and network loss are calculated to ensure the superiority of the proposed strategy. The simulation results show that the optimal charging strategy proposed not only reduce the cost of EV users but also reduce the power network loss rate. It achieves the win-win goal of EV users and electric power company.

Electrical Vehicle Wireless Charging Technology Based on Energy Internet Application in China

In response to global climate change as well as seeking for sustainable development, Electrical Vehicle (EV) is growing fast these years. During the new Energy Internet development era, EV will develop as one of the most important link between power system and transportation system. And the EV charging problem is always one of the major concerns on its developing way. The paper describes the present development situation of EV market in China. It introduces the wired charging status as well as the wireless charging development situation in China. It is expected that the EV will grow up to 5 million by 2020. Both the wired and wireless charging technology develops fast. When it comes to wireless charging, it has already some practical demonstration in operation around China. It aims to provide feasible development ideas for the integration of power system and transportation system under the unified Energy Internet framework in the future.

Oil-immersed Transformer Internal Thermoelectric Potential Fault Diagnosis Based on Decision-tree of KNIME Platform☆

Abstract The safety of power system is always affected by operating state of oil-immersed transformer directly. In order to improve reliability of power supply system, it is important to find and solve internal thermoelectric potential faults as soon as possible. In the past years, various of effective fault diagnosis techniques have been proposed, including chromatographic analysis, fault characteristic gases, IEC three-ratio method. Recently, artificial intelligence technique has been extensively used, such as neural network, genetic algorithm and so on. Although these methods have been applied in smart grid system effectively, we need a method with more accurate and more efficiency, visualization, and intelligence. So we proposed a method to solve internal thermoelectric potential fault diagnosis in this paper based on decision-tree C4.5 algorithm of KNIME platform. The experimental data from dissolved gas analysis (DGA) is used to illustrate performance of proposed decision-tree model. This designed model will obtain right results in short time by building a workflow in KNIME platform and convenient parameter setting which can ensure the normal operation of transformers.

Oil-immersed Power Transformer Internal Fault Diagnosis Research Based on Probabilistic Neural Network☆

Abstract Oil-immersed power transformer is one of the key devices in power system. And the reliability of power grid is guaranteed by its safe operation. Therefore, it is necessary to reduce transformer failures with precautionary measures. Nowadays, three-ratio method of dissolved gas analysis (DGA) is the most effective and convenient method in transformer fault diagnosis. However, when using three-ratio method as the judgment, it exists some disadvantages such as coding defects and threshold criterion defect. A new way for this problem is provided by artificial neural network, which has the advantages such as parallel processing, self-adaptation self-study, association memory, non-linear mapping and other features. Oil-immersed transformer internal faults are predicted in this paper by using probabilistic neural network algorithm, which brings its ability of processing non-linear problem into full play. Whats more, the DGA judgment process is optimized and convenient setting of parameters is achieved. The high accuracy of diagnosis is confirmed by simulation results in KNIME platform.