Machinery, materials science and eningeering applications

Tai ngay!!! Ban co the xoa dong chu nay!!! MACHINERY, MATERIALS SCIENCE AND ENGINEERING APPLICATIONS MMSE216_Book.indb i 2/14/2017 11:49:23 AM MMSE216_Book.indb ii 2/14/2017 11:49:23 AM PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS SCIENCE AND ENGINEERING APPLICATIONS (MMSE 2016), WUHAN, HUBEI, CHINA, 28–29 OCTOBER 2016 Machinery, Materials Science and Engineering Applications Editors Fei Lei China University of Geoscience, China Qiang Xu Huddersfield University, UK Guangde Zhang Wuhan University of Science and Technology, China MMSE216_Book.indb iii 2/14/2017 11:49:23 AM CRC Press/Balkema is an imprint of the Taylor & Francis Group, an informa business © 2017 Taylor & Francis Group, London, UK Typeset by V Publishing Solutions Pvt Ltd., Chennai, India Printed and bound in China by CTPS DIGIPRINTS All rights reserved No part of this publication or the information contained herein may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, by photocopying, recording or otherwise, without written prior permission from the publisher Although all care is taken to ensure integrity and the quality of this publication and the information herein, no responsibility is assumed by the publishers nor the author for any damage to the property or persons as a result of operation or use of this publication and/or the information contained herein Published by: CRC Press/Balkema P.O Box 11320, 2301 EH Leiden, The Netherlands e-mail: Pub.NL@taylorandfrancis.com www.crcpress.com – www.taylorandfrancis.com ISBN: 978-1-138-02957-6 (Hbk) ISBN: 978-1-315-37512-0 (eBook) MMSE216_Book.indb iv 2/14/2017 11:49:23 AM Machinery, Materials Science and Engineering Applications – Lei, Xu & Zhang (Eds) © 2017 Taylor & Francis Group, London, ISBN 978-1-138-02957-6 Table of contents Preface xi Committees xiii Introduction xv Material science and advanced materials A PEG-400-etherified 2D resin improves the water absorption property and thermal stability of silk fibres H Ding, Y He, Y.K Jiang, Y.Q Wu & X.X Yang Selective formation of AlPO4-21 and AlPO4-12 molecular sieves by microwave technique P.M Chang 15 Parameter optimization of forming limited diagram based on virtual material method Q Yu, J Liang & L.-W Tian 21 Effects of CaTiO3 addition on the microstructure and microwave dielectric properties of α-CaSiO3-SiO2 ceramics W Hu, C.X Yin, H.T Sui, W.X Shi, H.J Sun & S.F Huang 27 Research on the biomimetic materials with highly oriented animal bone microstructures Y.W Yuan, J Yu & L.M Yan 33 The damage variable and tensile properties of T2 pure copper with plastic strain L Li & Z.M Shi 39 Electrical engineering and automation control The network control technology of the shipbuilding gantry crane based on PLCs F Zhu, M Zhou, J.H Huang & J.H Huang 47 Development of a certain detecting device for a NCB proofing system based on an ADS7825 converter D.B Zhang, C.A Qiao, Y Liu, W.Q Huang & X.Y Li 53 A classification recognition method based on an improved kernel function support vector machine W.W Xiong, W Liang & M.X Su 59 Application of fuzzy comprehensive evaluation in fault diagnosis of plunger pump C Wu, D Wu, X.Y Mao, H.Y Ding, J.H Hu & Y.O Liu 67 v MMSE216_Book.indb v 2/14/2017 11:49:23 AM A study on the time–space characteristics of soil resistivity of substations in China X Xu, X Liu, S Wang, Z.Z Li, G Liu & Z Wang 73 Identification of rotary kiln cylinder bending by using wavelet and fourier transforms L Qin, Y Zhang & Q Peng 81 Electrical device fault diagnosis system of a specific device D.B Zhang, Y Liu, C.A Qiao & W.Q Huang 87 The optimization of wind power interval forecast X.D Yu & H.Z Zang 93 A diagnosis method for motor bearing faults based on harmonic injection B Feng, C.D Qiu, C.Q Xu & X.B Wu 103 Restoring force characteristic for motor bearing fault based on dynamic analysis R Guo, C.D Qiu, X.B Wu & C.Q Xu 109 A design of the totem pole output circuit for current mode PWM controllers D Zhao 115 Adaptive PD controllers for large cargo ships L.L Wan 119 Research on a new algorithm for the robot tracking problem Y.F Liu, T.M Zhang, J.J Yang & Q Tian 123 A study on the engine bench test system based on virtual instruments Z Zhang, J.M Yang, Y.F Zhang & R.B Zhou 129 Study on path planning of emergency rescue low altitude aircraft L.-L Wan & W.-P Zhou 133 Research on a real-time X-ray imaging detector for fuse safety D.B Zhang, C.A Qiao, Y Liu & W.Q Huang 141 Modeling and analysis of active heave compensation control in marine cranes F Zhu & J.H Huang 147 Identifying the metal magnetic memory signal feature for stress concentration zone J Zhang & S.Z Zhu 153 Command-filtered backstepping stabilization of nonlinear systems with quantized input X Yu & P Zhuang 161 Application of genetic algorithm-support vector regression for estimating modal damping Z.W Xia, X.T Wang, K.J Mao, G.S Shao, H.Y Ren & Y.Y Fang 169 Full-waveform current differential protection based on optical current transformers K Yue, G.-Q Zhang, C Yang, Z.-Q Liu & D Yin 175 Numerical calculation of the infinite element method in an inhomogeneous medium Y.Q Dun, Y Kong & Y.L Wang 181 Research on the simulation of the PUMA 560 robot based on Matlab Z.Y Wei, C.H Zhu & Z Yang 187 A scheme on the force and motion control of manipulator robots based on a neural network W Fang, Z Yang & Z.Y Wei A method of vehicle counting based on interframe difference method X.W Han & D.H Xu 193 199 vi MMSE216_Book.indb vi 2/14/2017 11:49:23 AM Electronic engineering Research on a low-cost ultra-wideband frequency measurement circuit design Q Li, Z Wang, J.L Wang & J.Y Li 207 Earphone antenna for handheld digital audio broadcasting receivers H.S Zhang, G.Y Wang, M.Y Lu & R.Y Zhu 213 Applied mechanics Validation studies of passive control for flexible wing gust alleviation Z.G Chen, Y.J Yan, W Han & H.F Li 223 A modeling method of the bolted joint structure and analysis of its stiffness characteristics G.Q Jiang, J.W Li & G.J Tang 229 An experimental study on the propulsive performance of a bionic dolphin tail fin C Ma, L Sun, C.-X Bian, C.-K Ding & X.-Y Shen 237 A study on the lubrication performance of end faces with diamond macro-pores X.P Cheng, L.P Kang, Y.L Zhang, B.L Yu, X.K Meng & X.D Peng 241 A study on the performance of self-adaptive mechanical seals under variable working conditions X.P Cheng, Y.L Zhang, L.P Kang & B.L Yu Bolt connection simulation based on MSC NASTRAN B.L Chen & Z.Q Xie 249 255 Mechanical engineering A new design on drive trains for light weight robotic arms H.B Yin, M.C He, S.S Huang & J.F Li 261 A study on vibration responses of mechanical systems with an impact on clearance joints Z.F Bai, X Shi, J.J Zhao & J Chen 273 A reconfigurable multi-spindle box for holes machining Y.Q Wang & G.P Zhang 281 Large deformation structure analysis of complex telescopic boom system for engineering machinery L Xu, X.D Xu, Z.J Tian & C.J Jin 289 Master–slave control of variable parameters of a vehicle hydraulic system support H Lu, L.C Shi, Q.Y Wei, E.D Mao & P.L Li 297 Experimental study on the vibration tapping of a titanium alloy J.Z Zhang, Y.X Hu, P Liu & Z.P Tian 305 Research on the optimization of the numerical control machine in engraving X.-Y Liang, J Zheng & S Wang 311 UHV breaker hydraulic line dynamic characteristics simulation analysis X.H Zhang 319 The design of the truss-type floating raft system and study of its vibration isolation characteristics Y.Y Fang, Y.Y Zuo, K.J Mao & Z.W Xia Research on the dual-driving synchronous control of the gantry machine W Fan, H Lu, Y.Q Zhang, H Ling, Y.X Niu & M Duan 325 333 vii MMSE216_Book.indb vii 2/14/2017 11:49:23 AM Aerospace science and technology The research of reliability of D subminiature connectors coupling in assembly and integration process of spacecraft W Zhang, Y.G Liu, Y.E Wei, D.M Wang & J Wang 343 Mechatronics Navigation of autonomous ground vehicles in cluttered and unknown environment L.W Zhang & L.J Zhang The design of a multiple hydraulic components test stand system based on mixed field bus technology J.G Yi & S.W Ju 351 359 Computer science and information technology Research on interface circuits and calibration algorithms for resistive touch screens C.L Tan, H.D Lei & Y.T Ye 367 Weights information analysis of readers’ needs in the university library C.J Xu 373 Design of portable maintenance aids based on the signal test and IETM X Zhou & H.-Y Zhao 379 A solution of information integration and service sharing in aerospace TT&C systems F Guan 385 Exploitation and realization of 3D virtual scene based on DirectX 11 technology Q Yuan & H Zhang 391 A target detection algorithm based on dynamic background compensation L.M Ye & Y Zhu 399 Graphics, visual and image analysis An extension of the quartic Wang–Ball closed curves with a given tangent polygon C.W Wang & H Chen 409 3D reconstruction and measurement using Kinect Y Zhao & E Wei 415 Monitoring and communications A laboratory environment monitoring system based on ZigBee Y.T Ye Research on the improved method of the ICI cancellation algorithm based on MMSE criteria T.F Liu, X.Y Song, X.P Ma, H.H Dong & L.M Jia 425 431 Computing A study of an online dynamic workload prediction algorithm in the cloud environment Y.Q Wang & C.X Fan 441 Text information extraction using a cluster-based hidden Markov model Y.Y Wang, X.D Lv & Q Hu 449 viii MMSE216_Book.indb viii 2/14/2017 11:49:23 AM An intensive study and experiment on the characteristics of NSGA-II Q Yuan & H Zhang 455 A model of security policies on cloud computing C Luo 465 Signal processing Effects of rain noise from metal roofs on speech identification Y Chen & X Li 473 Design and implementation of an ECG signal generator based on STM32 Z.J Meng, S.Y Huan & Y.T Ji 479 Fuzzy comprehensive evaluation Aquifer water yield capacity evaluation using fuzzy evaluation-comprehensive weighting method K Xu & Z Wei 489 Applied electrochemistry and analytical chemistry The influence of Al foil current collectors on electrochemical properties of LiFePO4-based Li-ion batteries J.C Wang, W He, X.D Zhang, Y.K Hou, Z.L Zhang & H Guo 497 Determination of imidacloprid, carbendazim and acetamiprid residues in dried longans by using HPLC L Lin, M.Y Wang, L.L Liu & C.L Yang 503 Determination of mycotoxin contaminants in juice by using LC-MS/MS Y.B Zha, C.L Yang, J.Z Ye, X.F Wang, L Lin, S.D Zeng, Y.Q Huang & Y.P Su 509 Geosciences Evaluation of groundwater quality in water source areas of a mining area by using the Nemero index method W.C Wang, J.W Zhao, P Fu, B.S Liang & Y Chen 515 Modern industrial technology Application of 3D printing technology in architect digital construction Y.Y Guo & J Zhang An analysis of the influential factors of the choice of the pattern of ordering cars online based on ISM and AHP X Sun, H.H Dong, A.L Huang, Y.F Yang, Y Qin & L.M Jia 525 531 A study on development strategies of China’s manufacturing service L Wang 539 Study on the application of “3D printing” in interior decoration Y Li 543 Study on the technical applications of museum interactive display from spectators’ behavioral experience perspectives—an example from Shanghai Science and Technology Museum J Zhang & Y.Y Guo 549 ix MMSE216_Book.indb ix 2/14/2017 11:49:23 AM MMSE216_Book.indb ii 2/14/2017 11:49:23 AM Machinery, Materials Science and Engineering Applications – Lei, Xu & Zhang (Eds) © 2017 Taylor & Francis Group, London, ISBN 978-1-138-02957-6 Simulation and experimental study of the three-phase three-legged transformer under DC bias Dong Xia School of Electrical Engineering and Automation, Qilu University of Technology, Jinan, China ABSTRACT: In this paper, a novel coupled model, which includes the electric circuit and the magnetic circuit of the three-phase three-legged transformer under Direct Current (DC) bias, is built In a magnetic circuit model, the eddy currents, the iron core topology, and the saturation characteristics of the core material are taken into account The magnetomotive forces due to the eddy currents are given in the magnetic circuit equations By combining the electrical circuit with the magnetic circuit and considering the non-linear characteristic curve of the iron core, the non-linear equations can be obtained A series of mathematics transformation is performed and the numerical non-linear algebraic equations are solved And then, the experimental work of the three-phase three-legged transformer under DC bias has been carried out Finally, the simulation results and the experiment results are compared They are in good agreement Meanwhile, the DC bias problem on the three-phase threelegged transformers is analyzed It demonstrates the validity of the simulation model and the accuracy of analyzing the DC bias phenomenon of the three-phase three-legged transformer INTRODUCTION The DC bias phenomenon is an abnormal work station of transformers There are two reasons, which cause DC bias One is the monopolar ground circuit operation mode or the bipolar asymmetrically operation mode of the HVDC transmission system [1–2] In this case, DC will pass by the windings of the transformers through the earthed neutral point The other is the Geomagnetically Induced Current (GIC), which is caused by the interaction with the geomagnetic field and the dynamic movement of the ionic wind [3] The change of the geomagnetic field produces an electric potential gradient, which gives rise to low-frequency induction current It is DC because its frequency is very low The work point of the transformer under DC bias will shift, and its iron core material will be saturated, which affects the normal work of the transformer seriously This produces lot of bad influence, for example, the distortion of the exciting current; the heavy noise; the serious vibration; the increase of the transformer’s leakage and losses; the local heating of metal structures; the malfunction of relay protection devices of the electric power system, etc And so, the DC bias phenomenon of the transformer is concerned Some simulations and experiments have been carried out by a lot of researchers The main research contents included four aspects, which are as follows: (1) experimental research of the vibration, great noise, and local high temperature of the transformer [4–6]; (2) harmonics analysis of the exciting current [7–9]; (3) measurement of magnetization characteristics of ferromagnetic materials [10]; (4) research on restrain measures of DC bias [11–12] Some achievements and progress have been obtained in the above-mentioned aspects The eddy currents, the iron core topology, and the saturation characteristics of the threephase three-legged transformer under DC bias have to be considered sufficiently In this paper, a novel coupled model, which includes the electric circuit and the magnetic circuit considering the above factors, is built The magnetomotive forces due to the eddy currents are included in magnetic circuit equations By coupling these equations with the electrical circuit equations, the non-linear equations are obtained The analysis of the three-phase three-legged transformer under DC bias can be accomplished by solving the equations 571 MMSE216_Book.indb 571 2/14/2017 11:54:30 AM 2.1 THE COUPLED MODEL OF THE TRANSFORMER The model of the electric circuit The equivalent electric circuit of the no-load three-phase three-legged transformer is shown in Fig Here u1, u2, and u3 are the three-phase AC voltages R1a, R2b, R3c, R1A, R2B, and R3C are the resistances of the primary and secondary windings, respectively e1a, e2b, e3c, e1A, e2B, and e3C are the induced electromotive forces of the primary and secondary windings, respectively i1, i2, and i3 are currents of the primary windings L1a, L2b, and L3c are the equivalent leakage magnetic inductions, which couple with the primary windings L1A, L2B, and L3C are the equivalent leakage magnetic inductions, which couple with the secondary windings U0 is the DC voltage The equations can be derived as follows: u j U − R jpi j − L jp dii j dt = −ee jp N dφ j dt , j = 1, 2,3 (1) where small letter subscripts p represents the primary side parameters and Φj is the iron core legs magnetic flux of the transformer 2.2 The model of the magnetic circuit The equivalent magnetic circuit of the three-phase three-legged transformer is shown as Fig where Fa, Fb, and Fc are the magnetomotive forces produced by the primary winding current Rk and Φk (k = 1~5) are the reluctances and magnetic fluxes of the iron core legs and yokes R8 and Φ8 are the leakage reluctance and flux Fek (k = 1~5) are the magnetomotive forces produced by eddy currents In the magnetic circuit model, the eddy currents are considered The magnetomotive forces produced by eddy currents are given by the following equation: Figure Equivalent electric circuit of the three-phase three-legged transformer under DC bias Figure Equivalent magnetic circuit of the three-phase three-legged transformer under DC bias 572 MMSE216_Book.indb 572 2/14/2017 11:54:30 AM Fe ke dφ dt (2) ke = σ d 2l ⎛ Gdτ H oσ l ⎞ ⎛ dφ ⎞ + ⎟⎠ ⎜⎝ dt ⎟⎠ 12 A ⎜⎝ A − (3) where A and l are the cross-section area and the length of the iron core legs, respectively σ is the electrical conductivity d is the thickness of the laminations τ is the width of the laminations G = 0.1356 H0 is a parameter with dimensions A/m representing the internal potential experienced by domain walls in the magnetic laminations The non-linear equation of the core material with the single value curve is given by the following equation: B f (H ) (4) The non-linear reluctances of the magnetic circuit are given by the following equation: Rk = l l H = Aμ A B (5) The magnetic circuit equations (6) can be obtained according to Fig ⎧R1φ1 R4φ4 − R2 Fa + Fe Fb − Fe1 Fe ⎪R ⎪ 3 R5φ5 − R2 Fc + Fe Fb − Fe − Fe ⎨ ⎪R2 R8φ8 = Fb Fe ⎪⎩φ2 + φ4 + φ5 + φ8 = (6) By substituting (2) and (5) into (6) and carrying on transformation and reorganization, the matrix equation is obtained and given as follows: Rφ j F(j = ) (7) Finally, the non-linear differential-algebraic equations can be obtained from the equation (1) and (7) And then, they are solved iteratively by using the Newton–Raphson method By selecting the appropriate time step, the stability and accuracy of the solution can be assured SIMULATION RESULTS The main parameters of the double windings three-phase three-legged transformer are listed as follows: rated capacity is 1600 kVA, rated transformation ratio is 10 KV/1 KV, Figure Graph showing exciting current without DC bias 573 MMSE216_Book.indb 573 2/14/2017 11:54:30 AM cross-sectional area of the iron core and yoke is 0.2536 m2, length of the iron core is 2.8 m, and length of the yoke is 1.8 m Figs and show the exciting currents of the transformer It can be seen that the current of the middle leg is smaller than that of the left and right legs, because the magnetic coupling of the Figure Graph showing exciting current with DC bias (IDC = 0.298 A) Figure Graphs showing non-linear characteristic curves without DC bias 574 MMSE216_Book.indb 574 2/14/2017 11:54:32 AM Figure Graphs showing non-linear characteristic curves with DC bias (IDC = 0.298A) middle leg with other legs is serious And the three-phase exciting currents shift up because the DC biasing current The bigger the DC biasing current is, the more serious the exciting currents shift It can also be seen that the waveforms of exciting currents basically not distort, and only the DC current component is added up to the three-phase exciting currents, respectively So there is almost no effect on the power system But the DC current component can increase the copper losses of transformer Figs and show the non-linear characteristic curves of the transformer It is obvious that the DC intrusion does not have an impact on the non-linear characteristic curve EXPERIMENTAL STUDY Fig shows the measurement circuit of the three-phase three-legged transformer under DC bias, where u1, u2 and u3 are the three-phase AC voltages, Re is the internal resistance, U0 is the 575 MMSE216_Book.indb 575 2/14/2017 11:54:32 AM Figure Schematic of the measurement circuit of the transformer Figure Graphs showing a comparison of the exciting currents without DC bias DC power supply, and R is the variable resistance The capacity of the transformer is 200 VA and the ratio is 380 V/38 V Figs and are the comparative figures of the simulation results and the measurement results It can be seen that the waveforms are in good agreement 576 MMSE216_Book.indb 576 2/14/2017 11:54:32 AM Figure Graphs showing a comparison of the exciting currents with DC bias CONCLUSIONS Each phase flux of the three-phase three-legged transformer interacts with each other In the mathematical model, the iron core topology, the saturation characteristics of the core material, and the influence of the eddy currents are considered sufficiently under DC bias The exciting currents and the non-linear characteristic curves not distort when the DC current invades into the three-phase three-legged transformer The simulation results and experiment results are compared and they are observed to be in good agreement REFERENCES [1] M.A.S Masoum, P.S Moses, 2010 Impact of balanced and unbalanced direct current bias on harmonic distortion generated by asymmetric three-phase three-leg transformers [J] IET Electr Power Appl 4(7):507–515 [2] Yang Yongming, Liu Xingmou, Chen Tao, et al 2012 Impact of soil structure adjacent to ground electrodes of UHVDC power transmission lines on DC bias of power transformers [J] Power System Technology, 36(7):26–32 [3] Cui Mingde, Liu Chunming, Liu Lianguang 2010 Assessment of the influence caused by solar storm on sichuan power grid rated 500 kV [J] High Voltage Engineering, 36(11):2849–2855 577 MMSE216_Book.indb 577 2/14/2017 11:54:33 AM [4] Wang Jin-gang, Mao Kai, Duan Xu, et al 2015 Simulation and test of transformer vibration under DC bias [J] Electric Machines and Control, 19(1):58–67 [5] Zhao Zhigang, Liu Fuguil, Cheng Zhiguang, et al 2011 Eddy current Loss of Copper Shielding Under DC-biased Condition in HVDC [J] High Voltage Engineering, 37(4):990–995 [6] Guo Jie, Huang Hai, Tang Xin, et al 2012 Analysis on 500 kV Power Transformer Vibration Under DC Magnetic Biasing [J] Power System Technology, 36(3):70–75 [7] Li Xiaoping, Wen Xishan 2010 DC Bias Computation Study on Three-phase Five Limbs Transformer [J] Proceedings of the CSEE, 30(1):127–131 [8] Zhao Xiaojun, Zhang Xiaoxin, Li Huiqi, et al 2014 Frequency Domain Coupled Model between Magnetic and Electric Circuits of DC Biased Transformers by Harmonic Balance Method [J] Transactions of China Electrotechnical Society 29(9):211–218 [9] Zhao Xiaojun, Li Lin, Cheng Zhiguang et al 2010 Analysis of the DC bias phenomenon in transformers based on harmonic-balanced finite element method [J] Proceedings of the CSEE, 30(21):103–108 [10] Zhao Z, Liu F, Cheng Z 2010 Measurements and calculation of core-based B-H curve and magnetizing current in DC-biased transformers [J], IEEE Transactions on Applied Super Conductivity, 20(3):1131–1134 [11] Zhu Yiying, Jiang Weiping, Zeng Zhaohua, et al 2005, Studying on Measures of Restraining DC current Through Transformer Neutrals [J] Proceedings of the CSEE, 25(13):1–7 [12] Huang FuCheng, Ruan Jiangjun, Zhang Yu, et al 2006, DC Magnetic Bias Induced Current Effects on Transformer and Restricting Methods [J] High Voltage Engineering, 32(9):117–120 578 MMSE216_Book.indb 578 2/14/2017 11:54:33 AM Machinery, Materials Science and Engineering Applications – Lei, Xu & Zhang (Eds) © 2017 Taylor & Francis Group, London, ISBN 978-1-138-02957-6 Microgrid energy management system and intelligent control technology Hongxia Wu Wuhan Donghu University, Wuhan City, Hubei Province, China ABSTRACT: As an effective measure of accommodating distributed generation, microgrid has attracted more and more attention recently Energy management system is one of the key technologies of micro power grid; it can ensure stability of the microgrid operation and adjust the output of each micro power unit, thereby making the maximum use of renewable energy and consuming less fuel On the basis of the characteristics of microgrid and its energy management system, this paper analyzes the application of intelligent control technology, such as genetic algorithm, immunity algorithm, and others, in a microgrid energy management system INTRODUCTION In recent years, the international community has placed great importance to the development and utilization of renewable energy due to the energy crisis, environmental pollution, and the impact of climate change Distributed generation is an effective way of development and utilization of renewable energy due to less pollution, high energy efficiency, flexible installation location, short construction period, simple maintenance, and so on [1, 2] However, the large-scale distributed generation will have an impact on the power grid and the users with the increase of distributed generation capacity when it is connected to the power grid The microgrid can coordinate the contradiction between power grid and distributed power supply, and can fully tap the value and benefits of distributed power supply, in order to improve the utilization of renewable energy [3, 4] Microgrid energy management system is the coordination control center of the microgrid and important guarantee for the safe and economic operation of microgrid On the basis of the characteristics of microgrid and its energy management system, this paper analyzes the application of intelligent control technology such as genetic algorithm, immunity algorithm, and others in microgrid energy management system OVERVIEW OF MICROGRID AND THE ENERGY MANAGEMENT SYSTEM 2.1 Microgrid Microgrid is a small power distribution system composed of distributed power, energy storage device, energy conversion device, related load and monitoring, protection device, and so on It can transform huge number and various forms of distributed power grid problems to determine micro power grid interconnection using the key technologies of microgrid operation control and energy management Furthermore, it can reduce both the negative influence of intermittent distributed power supply on power distribution network and the difficulty of scheduling and managing distributed power supply; it can coordinate the contradiction between distributed power supply and large power grid effectively It also can meet the end user demand for power quality, power supply reliability, security, and so on Microgrid 579 MMSE216_Book.indb 579 2/14/2017 11:54:33 AM technology has realized the flexible and efficient application of distributed generation technology in medium and low voltage levels With the development of smart grid, the microgrid technology will become one of the key technologies to realize large-scale application of distributed power generation system in the future [5] Microgrid has two operating modes: (1) networking mode, where the microgrid is connected to the conventional power distribution network; and (2) island mode In the latter mode, when the power quality is not satisfied, the microgrid will be disconnected from the power grid and run independently when the grid fault is detected or power quality does not meet the requirements [6] 2.2 Microgrid energy management system According to IEEE, “Management System Energy (EMS) includes engineering, design, application and maintenance of power supply system, etc., so as to achieve the best performance of power system.” The main task of the microgrid energy management is to coordinate the work of the distributed power supply and load modules and to optimize the flow of energy and the utilization of microgrid system, in order to meet the requirements of user loads with the minimum cost based on load demand information, weather conditions, market information, and power network operation information It can not only guarantee real-time and reliable supply of load demand in the short term, but also realize the optimal utilization of energy and the economic and safe operation in the long term Microgrid energy management system is the core of the coordination control of the whole system and the foundation of the microgrid for optimizing management and improving economic performance [7] It is an effective way to solve the problems of voltage control, power flow control, load distribution, stability in the island mode, and so on It has functions such as power generation optimization, load demand management, and real-time data monitoring, and it can carry out intelligent control and automatic scheduling decision for microgrid The function diagram of EMS is shown in Figure Figure Function diagram of EMS 580 MMSE216_Book.indb 580 2/14/2017 11:54:33 AM ENERGY MANAGEMENT SYSTEM AND INTELLIGENT CONTROL TECHNOLOGY 3.1 Characteristics of the microgrid energy management Reasonable energy management and scheduling strategy are essential to ensure synchronous operation of the microgrid with large power grid under the networking mode and maintain the voltage and frequency of the system under the island mode After the grid is connected, in order to realize smooth switching between the grid and the island mode, it can make the microgrid system smoothly switch between the networking mode and the island mode There are many differences between the microgrid energy management and the traditional power system energy management because of the special structure of the microgrid, which are described as follows The microgrid operation mode is flexible, and the energy management target is different in networking state and island state In the networking state, the energy management focuses on the maximum operation efficiency of the microgrid in consideration of external information and the related constraints The primary task of energy management is to ensure the safe and stable operation of microgrid in the island state Most of the power supply for microgrid is distributed power, such as solar power and wind power, whose output is fluctuating and random This makes pre-arrangement of scheduling strategy and scheduling plan impossible, as in conventional power grid, thereby increasing the pressure of peak regulation and frequency modulation For economic considerations, the microgrid is equipped with a large number of energy storage devices to maintain the power and energy balance The cost of energy storage equipment is relatively high, and its service life is related to the charging and discharging strategy The principle of energy storage devices influences the economic performance of microgrid directly There are numerous scheduled micro power units in microgrid; it needs comprehensive consideration and optimal scheduling plan to ensure the economic optimizing operation of power grid The load types of microgrid are various, and the load demand response or demand side management can participate in the energy management of microgrid The energy management can provide grading service, and it will abandon the noncritical load or delay the response to their needs under special circumstances, in order to provide high-quality power supply for critical loads 3.2 Characteristics of the microgrid energy management 3.2.1 The current research status of energy management technology The current research scenario can be divided into single microgrid energy management and multi microgrid energy management The main purpose of the single microgrid energy management is to optimize the internal energy and supply power at minimum cost and high reliability The multi-microgrid energy management should consider not only the optimal utilization of each internal energy but also the optimal allocation of the whole system energy and the balance of supply and demand 3.3 Application of intelligent control technology in the energy management of microgrid The economic dispatch and optimal operation of microgrid is one of the important targets of energy management At present, scholars mostly focus on the energy management of the microgrid optimization operation The optimization objectives and constraints considered for the microgrid should be sufficient for the wide variety of devices It is difficult to make all the target to achieve optimal value, and controlling is more complex in the multiobjective optimization 581 MMSE216_Book.indb 581 2/14/2017 11:54:34 AM Intelligent control technology is mainly used to solve the control problem of complex system, which is difficult to solve by traditional methods Its object is usually a system or process with many complex characteristics, and the main characteristics of this kind of system or process are high uncertainty, high nonlinearity, and highly complex mission requirements, which makes it difficult to obtain satisfactory control performance by using conventional control methods and means Therefore, the intelligent control technology has been widely used in the energy management of microgrid At present, the commonly used mature algorithms are genetic algorithm, particle swarm optimization algorithm, and artificial immune algorithm 3.3.1 Particle swarm algorithm Particle swarm algorithm is a kind of evolutionary algorithm to simulate the behavior of birds It was proposed by James Kennedy of the United States and electrical engineer Russell Eberhart at the International Conference in 1995 It is a kind of multiagent algorithm, whose essence is to simulate crowd behavior It embarks from the random solution and searches for the optimal solution by iteration, and evaluates the quality of the solution through the fitness The whole process of searching for updates is to follow the current process of the optimal solution It has high robustness, high precision, fast convergence characteristics, and so on, and it has been widely used in electric power system scheduling, optimization of location, energy scheduling, and so on In ref [2], the energy coordination control strategy is proposed, which is based on improved particle swarm optimization algorithm This strategy can be used to determine the optimal power allocation of each distributed generation and achieve normal and efficient operation of the microgrid In ref [3], the energy system is studied under the island mode The study adopts the dynamic adjustment acceleration factor of adaptive particle swarm optimization algorithm, and it takes the highest and lowest cost of system reliability as the optimization objective and the micro power generation capacity, energy storage equipment capacity, power load balance, reliability, and lower limit as constraints This method can be used to optimize the energy distribution of the microgrid in different regions and provide effective suggestions 3.3.2 Immune algorithm Immune algorithm is a simulated evolutionary algorithm, which simulates the human immune system It has good convergence and robustness In the immune algorithm, the antibody is a feasible solution to the optimization problem, and the target function belongs to the optimization problem The affinity between antibody and antigen showed the feasible solution and the degree of adaptation to the target function and the degree of similarity between the antibodies showed the degree of similarity between the feasible solutions The antibody was screened by the degree of fit of antibody and the degree of antibody, and then the antibody was used to carry out genetic mutation, in order to update the antibody group, to accelerate the search to the global optimal solution In the process of calculation, to maintain the diversity of the population and the rapidity of calculation as a pair of contradictory existence, a balance must be achieved between them It can overcome the premature problem, jump out of the local optimal solution, and quickly converge, in order to meet the requirements of accuracy and speed In ref [4], a niche immune algorithm based on the evolution of B cell populations is put forward The computational speed of the immune algorithm and the diversity of the antibodies are considered separately in this paper, in order to improve the performance of immune algorithm If the convergence speed is low, the immune algorithm cannot accurately determine the location of the optimal solution when the immune algorithm is near to the optimal solution Under the two kinds of operation condition of microgrid networking mode and island mode, the energy management strategy is put forward with the minimum network loss and superior power quality requirements When the power supply is sufficient, the microgrid can achieve better activity and minimize reactive power loss 582 MMSE216_Book.indb 582 2/14/2017 11:54:34 AM 3.3.3 Predictive control Predictive control is a new type of computer control algorithm developed in recent years It is very convenient to establish the model, and it is not required to understand the internal mechanism of the process It adopts the control strategy of multistep test, rolling optimization, and feedback correction, so the dynamic control effect is good and robustness is high It is suitable for controlling the complex industrial production process, which is not suitable to establish accurate digital model It can also be applied in belt restriction, large pure delay, non-minimum phase, multiple input multiple output, nonlinear condition, and so on In ref [5], an energy optimization method based on Distributed Model Predictive Control (DMPC) is put forward The energy management model of the system is established based on the energy flow characteristics of the multi-microgrid By solving the distributed predictive control optimization problem based on the equilibrium of supply and demand balance error, the problems of effective utilization of the new energy and load demand in real time are solved Under the premise of guaranteeing the real-time requirement of the system, the computation quantity of the optimization problem is reduced 3.3.4 Genetic algorithm Genetic algorithm is a method of searching the optimal solution by simulating natural selection and natural genetics in the process of biological evolution It was proposed by Professor J Holland of University of Michigan in 1975, and it has the following characteristics: (1) The fitness function of genetic algorithm domain can be set arbitrarily and cannot be affected by continuous differentiable constraints; (2) Since it starts the search from a set of points, rather than from a single point, its coverage area is large and it is not easy to fall into local optimal solution; and (3) It is a kind of random search algorithm, whose search direction is determined by the rule of probability theory Its main characteristic is group search, and the information among individuals in the group is also constantly exchanged The search process has higher adaptability This algorithm has been widely used in power system planning and research The microgrid energy management model is established in ref [7] The constraint condition of the model is the output characteristic and voltage fluctuation range of the distributed power supply, and the objectives are the minimum power loss, minimum voltage deviation, and economic optimum of the whole microgrid The crossover operator and selection operator of genetic algorithm are improved by using the method of dynamic determination of mutation probability and optimal individual reservation strategy Finally, this method is proved to ensure the best technical index and the optimal economy of the microgrid through an example 3.3.5 Other intelligent algorithms Intelligent algorithms such as back propagation and chaos optimization algorithm can also be applied to the microgrid energy management system Back propagation is a supervised learning algorithm in artificial neural networks In theory, BP can approximate any function, and the basic structure is composed of a nonlinear change unit, with a strong nonlinear mapping ability, and the number of the middle layer, the number of processing units, and the network learning coefficient can be set according to the specific conditions, which has a wide application prospect in many fields such as optimization, signal processing and pattern recognition, intelligent control, and fault diagnosis A large number of studies show that the introduction of intelligent control technology can improve the control effect and control efficiency of the energy management system CONCLUSION Microgrid is an important way to realize self-healing, user interaction, and demand response in the future smart distribution network Energy management system is the core of the microgrid, and a good energy management system can reduce the impact of microgrid to large 583 MMSE216_Book.indb 583 2/14/2017 11:54:34 AM power grid effectively, so as to play the advantages of microgrid better Intelligent control technology can effectively solve the multiobjective optimization problem of energy management system, and hence it will be used more and more widely in the future REFERENCES [1] Bao Wei, 2014 Research on Control and Energy Management Strategy of Micro-grid Composed of Multi-Voltage Source Type Micro-sources, edited by A Dissertation Submitted to the Graduate School of China Electric Power Research Institute, Beijing [2] Yang Jia, 2013 Distributed Power self-Control Strategy and Coordination system in Microgrid, edtied by Southwest Jiaotong University, Chengdu [3] Liu Zi-qiu, 2015 Research on dynamic optimal energy allocation and energy management system in islanded mode micro-grid, edited by Zhe-jiang University, Hangzhou [4] Yang Yang, 2010 Energy Management and Control System Improvement for Microgrid, Shanghai Jiao Tong University, Shanghai [5] Xu Jun, 2014 Model Predictive Control for Energy Management of Microgrid, edited by East China University of Science and Technology, Shanghai [6] Jin Heng, 2011 Research on Control and Energy Management Strategies In Microgrid, edited by Harbin Institute of Technology, Harbin [7] Liu Hai-long, 2011 Research on improved genetic algorithm Control for Energy Management of Microgrid, edited by Taiyuan University of Technology, Shijiazhuang [8] R Lasseter, A Akhil, C Mamay, etc 2002 Integration of distributed energy resources: The CERTS microgrid concept, Consortium for Electric Reliability Technology Solutions, USA [9] Zhang Jian-hua, Huang Wei, 2010 Microgrid operation control and protection technology, edited by China Electric Power Press, Beijing [10] Global Wind Energy Council, in: Global wind report annual market update 2011, edited by Global Wind Energy Council, Brussels [11] European Photovoltaic Industry Association, in: Global market outlook for photovoltaics until 2015, edited by European Photovoltaic Industry Association, Brussels [12] Lasseter R, 2003 Distributed generation, edited by PSERC 584 MMSE216_Book.indb 584 2/14/2017 11:54:34 AM