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Coherent unambiguous transmit for sparse linear array with geography constraint

IET RADAR SONAR AND NAVIGATION 2017年第2期11卷 386-393页

作者： Yu, Xianxiang Cui, Guolong Yang, Shiqian Kong, Lingjiang Yi, Wei Univ Elect Sci & Technol China Sch Elect Engn Chengdu 611731 Sichuan Peoples R China

This study considers the sparse linear array (SLA) design problem of coherent unambiguous transmit for the distributed aperture coherence-synthetic radar, accounting for the antenna size constraint and geography constraint where the elements cannot be placed in a given region. The authors develop an objective function considering the sum of the peak sidelobe level (PSL) for different beam shifting directions. Then, based on a processing strategy for considered constraints, they derive the antenna element locations by minimising the objective function through particle swarm optimisation algorithm. Finally, several numerical simulations verify the effectiveness of the proposed SLA design method.

关键词： synthetic aperture radar radar antennas linear antenna arrays particle swarm optimisation numerical analysis coherent unambiguous transmit sparse linear array design problem geography constraint SLA design problem distributed aperture coherence-synthetic radar antenna size constraint objective function minimisation beam shifting directions processing strategy antenna element locations particle swarm optimisation algorithm numerical simulations

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Sidelobe-level suppression for linear and circular antenna arrays via the cuckoo search-chicken swarm optimisation algorithm

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IET MICROWAVES ANTENNAS & PROPAGATION 2017年第2期11卷 209-218页

作者： Liang, Shuang Feng, Tie Sun, Geng Jilin Univ Coll Software Changchun 130012 Jilin Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Jilin Peoples R China

An antenna array application with high directivity and low sidelobe level (SLL) is an important technology that can enhance the reliability and validity of a communication system. In recent years, swarm intelligence optimisation algorithms have been widely used in the design of antenna arrays. In this study, a hybrid heuristic swarm intelligence optimisation algorithm called cuckoo search-chicken swarm optimisation (CSCSO) is proposed to optimise the excitation amplitude of a linear antenna array (LAA) and the excitation amplitude and spacing between the array elements of a circular antenna array (CAA). The maximum SLL will be obtained while the mainlobe width is fixed. CSCSO combines cuckoo search (CS) and chicken swarm optimisation (CSO) and thus, it will have the excellent global search capability of CS as well as introduce the hierarchy mechanism of CSO to improve algorithm precision. Chaos theory is utilised to determine the initial solution of the algorithm, while Levy flight weight coefficient is used to improve the convergence rate in CSCSO. Simulation results show that CSCSO has a better performance in terms of solution accuracy and convergence rate in the radiation pattern optimisation of LAA and CAA compared with the standard CSO, CS, and particle swarm optimisation algorithms.

关键词： linear antenna arrays search problems antenna radiation patterns optimisation sidelobe-level suppression cuckoo search-chicken swarm optimisation algorithm SLL suppression reliability communication system CSCSO LAA CAA linear antenna array circular antenna array chaos theory Levy flight weight coefficient radiation pattern optimisation particle swarm optimisation algorithm

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Multi-objective optimal design of passive power filter for aircraft starter/generator system application

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JOURNAL OF ENGINEERING-JOE 2018年第13期2018卷 636-641页

作者： Zhao, Dan Liu, Weiguo Shen, Ke Zhao, Guodong Wang, Xin Northwestern Polytech Univ Sch Automat Inst REPM Elect Machines & Control Technol Xian Shaanxi Peoples R China

A multi-objective optimisation design method for passive power filter devices is proposed to solve the input current harmonic suppression problem of the aircraft starter/generator system. The parameters of the filter device are optimised by using the particle swarm optimisation algorithm. The proposed algorithm takes harmonic parameters, DC voltage drop, total volume and weight as the optimisation target. With these optimisations, the filter has the minimal volume and weight, while the current harmonic and the DC side voltage drop are able to meet the power quality requirements. This approach improves the filter design procedure with advantages of the reduced amount of calculation. Finally, theoretical analysis and computer simulation validate the effectiveness and viability of the presented algorithm.

关键词： starting passive filters power supply quality particle swarm optimisation aircraft power systems power harmonic filters minimal volume DC side voltage drop power quality requirements filter design procedure multiobjective optimal design multiobjective optimisation design method passive power filter devices input current harmonic suppression problem particle swarm optimisation algorithm harmonic parameters DC voltage drop optimisation target aircraft starter-generator system application

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Vehicle chaos identification and intelligent suppression under combined excitation of speed bump and engine

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JOURNAL OF ENGINEERING-JOE 2018年第16期2018卷 1595-1599页

作者： Hu, Qi-guo Yuan, Shuai-hui Han, Ao Chongqing Jiaotong Univ Coll Electromech & Automobile Engn Chongqing Peoples R China Chongqing Jiaotong Univ Coll Transportat Chongqing Peoples R China

For non-linear suspension, vehicle passes through a continuous speed bump, the chaos that may occur under the combined excitation of the speed bump and the engine. This study takes the five-degree-of-freedom vehicle model as the object of research, through the vehicle body poincare section and the maximum Lyapunov index to identify the chaos produced by the vehicle under joint excitation, and utilises the feedback control of the optimal feedback gain coefficient based on the particle swarm optimisation (PSO) algorithm to suppress vehicle chaos. The results indicate that the vehicle is in a chaotic state in all speed range. Under low and medium speeds, the route to the chaos of the vehicle is the system coupling vibration under multi-frequency excitation, whereas in the high-speed condition, the vehicle approaches the chaos through the bifurcation. The chaos of the vehicle can be effectively suppressed by feedback control with the global optimal feedback gain searched by PSO. This study reveals the chaotic characteristics of non-linear suspension vehicles under combined excitation, which provides a new method for intelligent suppression of chaos.

关键词： suspensions (mechanical components) Lyapunov methods chaos feedback particle swarm optimisation bifurcation vibrations vehicle dynamics road vehicles combined excitation bifurcation system coupling vibration Lyapunov index vehicle chaos speed bump global optimal feedback gain speed range particle swarm optimisation algorithm optimal feedback gain coefficient feedback control five-degree-of-freedom vehicle model intelligent suppression

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Fault recovery for distribution network with distributed generation

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The Journal of Engineering 2018年第13期2017卷 2384-2388页

作者： Wei Ba Yuming Yan Kangda Wang Bingqing Wu Zhenghong Chen Xueying Wang Department School of Electrical Engineering Xi'an Jiaotong University Xi'an People's Republic of China

Distributed generation (DG) has been largely used in distribution network in recent years, which brings more security, reliability, and economy, but some bad effects to the distribution network. In this study, recovering more loads, less switching actions and distribution network power loss are considered to establish the mathematical model of fault recovery in distribution network with DG. The particle swarm optimisation algorithm is used to solve the problem, and the fault recovery strategy is obtained. Besides, the impact of DG location on the fault recovery of distribution network is studied, and the simulation results show that when the DG is located dispersedly in the downstream of the distribution network, the distribution network recovery is in good condition. The results of this study provide a reference for the DG configuration of the distribution network.

关键词： power distribution faults distributed power generation particle swarm optimisation distributed generation distribution network power loss mathematical model particle swarm optimisation algorithm fault recovery strategy DG location

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Harmonic control optimisation model considering power generation of microgrid

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The Journal of Engineering 2018年第13期2017卷 1565-1569页

作者： Ning Wang Kai Wei Jie Yuan Qingquan Jia College of Electrical Engineering Yanshan University Qinhuangdao 066000 Hebei Province People's Republic of China

In a microgrid with a large amount of photovoltaic (PV), the harmonic pollution problem is mostly caused by the non-linear load in the PV system and the microgrid. The multi-function grid-connected inverter (MFGCI) and the active power filter (APF) used to control the harmonic pollution is integrated into the framework of the bi-layer planning model in this paper. The upper layer model of the bi-layer optimisation is based on the cost of the microgrid power generation with PV as the objective function. Consider the maintenance costs of PV power generation and MFGCI equipment, line loss costs, purchase expectations and sales period expectations. The lower objective function is the minimum root mean square of the harmonic distortion rate of each node of the microgrid satisfying the MFGCI and APF capacity allocation constraint. The improved particle swarm optimisation algorithm and Latin hypercube technology are used to solve the bi-layer planning model. The results show that in the microgrid, the integrated optimisation method based on the bi-layer planning model is optimised for MFGCI and APF. It has a significant effect on the efficient operation of the microgrid and the improvement of power quality level of the microgrid.

关键词： least mean squares methods particle swarm optimisation invertors power harmonic filters photovoltaic power systems distributed power generation objective function PV power generation line loss costs purchase expectations sales period expectations minimum root mean square bi-layer optimisation bi-layer planning model harmonic pollution APF active power filter MFGCI multifunction grid-connected inverter PV system microgrid power generation harmonic control optimisation model power quality level Latin hypercube technology particle swarm optimisation algorithm harmonic distortion rate

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Point and interval forecasting of real-time and day-ahead electricity prices by a novel hybrid approach

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第9期11卷 2173-2183页

作者： Tahmasebifar, Reza Sheikh-El-Eslami, Mohammad Kazem Kheirollahi, Reza Tarbiat Modares Univ Dept Elect & Comp Engn Tehran Iran

Accurate forecasting of electricity market prices presents important information to market participants. This provides forward planning of their bidding strategies in order to maximise revenue, profit, and utility perspectives. Nevertheless, due to the non-stationarities involved in market clearing price, an accurate forecasting of these prices is very complex. In this case, transformation from traditional point forecasts to probabilistic interval ones is of great importance to quantify the uncertainties of potential forecasts. In this study, interval forecasting of market clearing prices is conducted based on a novel approach within two consecutive steps. In the first step, a new hybrid method is proposed to estimate point forecasts: combination of wavelet transformation (Wt), feature selection based on Mutual Information (MI), extreme learning machine (ELM), and bootstrap approaches in an ensemble structure is employed. The second step consists of the following stepwise parts: calculating the variance of the model uncertainties based on the extracted data from the ensemble structure, estimating the noise variance by using the maximum-likelihood estimation (MLE), and improving the accuracy of interval forecasting by using particle swarm optimisation (PSO) algorithm. The effectiveness of the proposed approach termed as Wt-mutual information-ELM-MLE-PSO is validated through electricity market real data of Australian electricity network from real-time and day-ahead market viewpoints.

关键词： pricing power markets tendering profitability wavelet transforms feature selection learning (artificial intelligence) statistical analysis maximum likelihood estimation power engineering computing particle swarm optimisation real-time electricity price point and interval forecasting day-ahead electricity price point and interval forcasting electricity market price forecasting bidding strategy forward planning revenue maximisation profit maximisation utility perspective market clearing price interval forecasting wavelet transformation combination feature selection extreme learning machine bootstrap approach model uncertainty variance calculation maximum-likelihood estimation particle swarm optimisation algorithm Wt-mutual information-ELM-MLE-PSO hybrid approach Australian electricity network electricity market real data

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New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features

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IET IMAGE PROCESSING 2017年第2期11卷 89-98页

作者： Fadaei, Sadegh Amirfattahi, Rassoul Ahmadzadeh, Mohammad Reza Isfahan Univ Technol Dept Elect & Comp Engn Digital Signal Proc Res Lab Esfahan *** Iran

A new content-based image retrieval (CBIR) scheme is proposed based on the optimised combination of the colour and texture features to enhance the image retrieval precision. This work focuses on a uniform partitioning scheme which is applied in the Hue, Saturation and Value (HSV) colour space to extract dominant colour descriptor (DCD) features. In the proposed CBIR scheme, the DCD features are initially extracted as the colour features, and then an appropriate similarity measure is applied. Also, several wavelet and curvelet features are defined as texture features to overcome the noise and the problem of image translation. Finally, the colour and texture features are optimally combined by using the particle swarm optimisation algorithm. The findings show that not only the proposed colour, wavelet and curvelet features outperform the existing ones but also their optimum combination has a better accuracy in comparison with several contemporary CBIR systems. The performance analysis shows that the proposed method improves the average precision metric from 67.85 to 71.05% for DCD, 58.90 to 65.43% for wavelet and 53.18 to 56.00% for curvelet using Corel dataset. In addition, the optimum combination presents the average precision of %76.50 which is significantly higher than the other state-of-the-art methods.

关键词： image retrieval content-based retrieval wavelet transforms curvelet transforms feature extraction image colour analysis image texture particle swarm optimisation content-based image retrieval system DCD optimised integration curvelet features wavelet features CBIR scheme image texture features image colour features uniform partitioning scheme HSV colour space dominant colour descriptor feature extraction image translation problem particle swarm optimisation algorithm Corel dataset

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Analysis for distribution network on hosting capacity of distributed wind turbines considering additional income under procedure conditions

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The Journal of Engineering 2018年第13期2017卷 1373-1377页

作者： Ye Wang Yuwei Yu Jie Zhang Anhui Huadian Engineering Consultating & Design CO Ltd No.122 Jinzhai Road Baohe District Hefei 230022 People's Republic of China CYG Sunri Co. Ltd Room 401 No. 2. Building Zin Entrepreneur R$D Centre No. 89 Shengli Road Jiangning District Nanjing 211100 People's Republic of China

An increasing number of wind turbines (WTs) are integrated into the distribution network to generate more electricity and reduce energy cost, which has a significant influence on the distribution network. So how to calculate the hosting capacity of WTs is of great importance. In this study, the analysis method was proposed to calculate the hosting capacity of WTs integrated into the distribution network under the procedure conditions. The authors firstly analysed several factors that imposed restrictions on the hosting capacity of the distribution network for WTs. Secondly, the authors built the optimisation model considering additional income, which aimed at reaching the maximum economic benefits by WT's access. Then the particle swarm optimisation algorithm, which could be applied to the problem, was given. In the calculation example, the authors analysed the different situation of WT's access to verify the proposed method. The installation points of WTs and the cost of investments were discussed to compare the economy of integration of WTs with the analysis results. The results showed that integrating WTs into the middle of lines could promote the economy and the decline in the cost of WTs also helped to enhance the hosting capacity of the distribution network for WTs.

关键词： wind turbines wind power plants distributed power generation power distribution economics power generation economics cost reduction particle swarm optimisation investment distribution network distributed wind turbines hosting capacity energy cost reduction particle swarm optimisation algorithm investments WT integration

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Electric vehicle modelling and energy-efficient routing using particle swarm optimisation

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IET INTELLIGENT TRANSPORT SYSTEMS 2016年第2期10卷 65-72页

作者： Abousleiman, Rami Rawashdeh, Osamah Oakland Univ Dept Elect & Comp Engn Rochester MI 48309 USA

Vehicle routing is traditionally based on Dijkstra or Dijkstra-like algorithms. These algorithms worked well for fossil fuel vehicles. The increase in pollution levels, government regulations, and pressure from environmental groups have caused an increase in electric vehicles (EVs) production and use. EVs are capable of regenerating energy which creates negative weights in search graphs that traditional algorithms are incapable of handling without some modifications. This study presents a model that characterises the energy consumption of an electric vehicle. Most passive and active factors are presented and applied in the formulation. The presented model is verified against 306 kilometres of driven data and proved to have 1.3% absolute error difference between the real vehicle's consumed energy versus the predicted energy consumption as generated by the model. The model is then used with a particle swarm optimisation algorithm to solve the single constraint optimisation problem of finding the most energy efficient route between 2 points on a map. Simulation and real-world test results demonstrate savings in the energy consumption of the electric vehicle. Results showed more than 9.2% reductions in the energy consumption of the electric vehicle when driven on the developed algorithms' suggested routes rather than the ones generated by Google Maps and MapQuest.

关键词： particle swarm optimisation electric vehicles vehicle routing energy conservation energy consumption search problems graph theory electric vehicle modelling energy-efficient routing vehicle routing Dijkstra-like algorithms fossil fuel vehicles pollution levels government regulations EVs production energy regeneration search graphs energy consumption active factors passive factors particle swarm optimisation algorithm single constraint optimisation problem

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