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Modular product design for additive manufacturing of satellite components: maximising product value using genetic algorithms

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CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS 2019年第4期27卷 331-346页

作者： Borgue, Olivia Panarotto, Massimo Isaksson, Ola Chalmers Univ Technol Dept Ind & Mat Sci S-41296 Gothenburg Sweden

For space manufacturers, additive manufacturing promises to dramatically reduce weight and costs by means of integral designs achieved through part consolidation. However, integrated designs hinder the ability to change and service components over time - actually increasing costs - which is instead enabled by highly modular designs. Finding the optimal trade-off between integral and modular designs in additive manufacturing is of critical importance. In this article, a product modularisation methodology is proposed for supporting such trade-offs. The methodology is based on combining function modelling with optimisation algorithms. It evaluates product design concepts with respect to product adaptability, component interface costs, manufacturing costs and cost of post-processing activities. The developed product modularisation methodology is derived from data collected through a series of workshops with industrial practitioners from three different manufacturer companies of space products. The implementation of the methodology is demonstrated in a case study featuring the redesign of a satellite antenna.

关键词： additive manufacturing satellite components modularisation product architecture optimisation algorithm

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Fast velocity trajectory planning and control algorithm of intelligent 4WD electric vehicle for energy saving using time-based MPC

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第1期13卷 153-159页

作者： Wu, Dong-mei Li, Yue Du, Chang-qing Ding, Hai-tao Li, Yang Yang, Xiao-bo Lu, Xin-yue Wuhan Univ Technol Hubei Key Lab Adv Technol Automot Components 122 Luoshi Rd Wuhan Hubei Peoples R China Jilin Univ State Key Lab Automot Simulat & Control 5988 Renmin Ave Changchun Jilin Peoples R China Tech Ctr Dongfeng Commercial Vehicle 10 Dongfeng Ave Wuhan Hubei Peoples R China

For intelligent four-wheel-drive (4WD) electric vehicle (EV), the vehicle speed can be planned and controlled for energy saving based on the slope information of road ahead. To reduce the calculation load of the optimisation algorithm, the model predictive control (MPC) method is formulated based on the time horizon in this study. Furthermore, a fast gradient method based control tool-GARMPC is used to solve the optimisation problem. First, the longitudinal dynamics model of 4WD EV based on time horizon and distance horizon is established based on the road slope information, respectively. Second, the MPC problem based on the time-discrete model is formulated and solved by GARMPC tool. For comparison, a dynamic program (DP) control method is introduced based on the distance-discrete model. Finally, the simulation is conducted under a designed road condition and a real measured road condition. The results show that the time-horizon based MPC method can significantly reduce the energy consumption compared with the proportion integration differentiation control method, which is similar to the driver's operation. Compared with the DP optimisation method, the time-based MPC method reduces the calculation time to smaller than 1ms, which is essential for real-time application in a road vehicle.

关键词： vehicle dynamics road vehicles trajectory control velocity control electric vehicles dynamic programming predictive control gradient methods energy conservation drives fast velocity trajectory planning algorithm fast velocity trajectory control algorithm intelligent 4WD electric vehicle intelligent four-wheel-drive electric vehicle energy saving time-based MPC vehicle speed control road slope information optimisation algorithm calculation load reduction model predictive control method fast gradient method based control tool GARMPC tool longitudinal dynamics model distance horizon time-horizon based MPC method time-discrete model dynamic program control method DP control method distance-discrete model

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Wide area oscillation damping controller for DFIG using WAMS with delay compensation

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IET RENEWABLE POWER GENERATION 2019年第1期13卷 128-137页

作者： Simon, Likin Swarup, K. Shanti Ravishankar, Jayashri Indian Inst Technol Elect Engn Dept Madras Tamil Nadu India Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Several studies have reported the improvement of system stability with a doubly-fed induction generator (DFIG) based wind turbines. Due to the remote location of DFIG, the local signals measured may not contain the complete oscillation information of the power system. This study describes an optimisation enabled wide area damping control (WADC) for DFIG to mitigate both local and inter area oscillations. Wide area measurement systems (WAMSs) which include phasor measurement units and synchrophasors are used for a centralised controller for damping inter-area and local oscillatory modes. The proposed damping controller parameters are optimised along with the local power system stabiliser settings for maximising the impact on system modal damping. The challenging shortcoming of WAMS based controller is the variable communication latency which can adversely affect the controller if not accounted in the controller design process. The proposed WADC algorithm addresses this issue and compensates for the delayed and time stamped error signals. The variable latencies are normalised in the optimisation algorithm in the design of controller parameters. The proposed algorithm is verified with transient simulation in PSCAD/EMTDC for the most severe three-phase faults. A set of comprehensive case studies are performed to analyse the effectiveness of the proposed algorithm for the noisy, delayed communication signals.

关键词： phasor measurement oscillations asynchronous generators control system synthesis power generation control wind turbines power system stability power system control damping power system interconnection wind power plants power system measurement wide area oscillation DFIG delay compensation system stability induction generator based wind turbines remote location local signals complete oscillation information wide area damping control local area oscillations inter area oscillations wide area measurement systems phasor measurement units synchrophasors centralised controller inter-area local oscillatory modes damping controller parameters local power system stabiliser settings system modal damping WAMS based controller controller design process WADC algorithm addresses this issue delayed time optimisation algorithm comprehensive case studies noisy delayed communication signals

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optimisation of controller parameters for grid-tied photovoltaic system at faulty network using artificial neural network-based cuckoo search algorithm

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IET RENEWABLE POWER GENERATION 2017年第12期11卷 1517-1526页

作者： Kalaam, Rahila N. Muyeen, S. M. Al-Durra, Ahmed Hasanien, Hany M. Al-Wahedi, Khaled Khalifa Univ Sci & Technol Dept Elect Engn Abu Dhabi U Arab Emirates Curtin Univ Dept Elect & Comp Engn Perth WA Australia Ain Shams Univ Elect Power & Machines Dept Fac Engn Cairo 11517 Egypt

This study exhibits the optimum design procedure to tune controller parameters for grid-connected distributed generation system based on cuckoo search algorithm (CSA). To investigate the effectiveness of proposed algorithm, a grid-tied photovoltaic (PV) system consisting of two power electronic converters controlled by five proportional integral (PI) controllers is chosen. Setting proper values for all the PI controllers is a complicated task, notably when the system is non-linear. In this study, response surface methodology (RSM) is used to develop the mathematical design of the PV system which is required to apply the optimisation algorithm. To minimise the design efforts of RSM, an alternate approach based on artificial neural network is introduced to develop the mathematical model of the PV system which is another salient feature of this research. Moreover, two modifications in the CSA are proposed to extract optimum parameters for the controllers which are found suitable in power system applications. Both the transient and dynamic performances of the system with the optimum values obtained through CSA are studied for different types of grid fault conditions using PSCAD/EMTDC. The design values are compared with values obtained through genetic algorithm and bacterial foraging optimisation. Experimental validation is also given for the proposed method.

关键词： photovoltaic power systems power generation control search problems neurocontrollers control system synthesis power grids power convertors PI control nonlinear control systems response surface methodology power system transients power generation faults controller parameter optimisation grid-tied photovoltaic system faulty network artificial neural network cuckoo search algorithm optimum design procedure grid-connected distributed generation system CSA grid-tied PV system power electronic converters proportional integral controllers PI controllers nonlinear system response surface methodology RSM mathematical design optimisation algorithm mathematical model optimum parameters extraction power system applications transient performances dynamic performances PSCAD EMTDC model grid fault conditions

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An improved adaptive cuckoo search algorithm based on the population feature and iteration information

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INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS 2020年第3期24卷 233-248页

作者： Jia Chaochuan Yang Ting Wang Chuanjiang Fan Binghui He Fugui Shandong Univ Sci & Technol Coll Mech & Elect Engn Qingdao 266510 Shandong Peoples R China West Anhui Univ Coll Elect & Informat Engn Luaan 237012 Anhui Peoples R China

Cuckoo search (CS) is widely used to solve many optimisation problem, which is a biologically inspired the brood parasitic behaviour of a type of cuckoos and the Levy flights behaviour of some animals. However, it has been demonstrated to easily get trapped into local optimal solutions and slow convergence speed. Therefore, an improved adaptive cuckoo search (IACS) optimisation algorithm is proposed in this article. Two adaptive strategies based on the population feature and iteration information feedback which are integrated into the CS algorithm to adjust the parameters p(a) and alpha(0). We compared the proposed algorithm to CS and five variants on the 30 benchmark functions proposed in CEC 2014. In addition, the proposed algorithm and CS are integrated into support vector machine (SVM) for classification. Experimental results certify that the modified algorithm is superior to the CS for most optimisation problems and has better performance than the other variants of CS algorithm.

关键词： cuckoo search optimisation algorithm adaptive strategy support vector machine SVM classification

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Parameters and control optimisation of hybrid vehicle based on simulation model

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JOURNAL OF ENGINEERING-JOE 2019年第13期2019卷 93-97页

作者： Wang, Jiayi Liu, Xinhui Wang, Xin Sun, Kui Zheng, Boyuan Jilin Univ Sch Mech & Aerosp Engn Changchun Jilin Peoples R China Bosch Automot Components Changchun Co Ltd Changchun Jilin Peoples R China

It is generally difficult to obtain accurate optimisation functions during parameters matching and optimisation process of the hybrid system, especially in the optimisation variable field containing both a continuous set and a discontinuous set and when the control strategy has an important influence on the system performance. To aim at the fuel economy of the parallel hydraulic hybrid system, a global parameter matching and control strategy based on the simulation model were proposed. The AMESim simulation model as the judgment module of the optimisation algorithm was used to match and optimise the main parameters and control strategies in the hybrid system. The communication of underlying parameters was developed by the VBA in EXCEL platform. Parameters match was implemented on a truck crane chassis in practice. The test results indicated that the rate of fuel saving reached 15.2%, which was generally consistent with the theoretical analysis. The parameters were met the energy-saving requirements of hybrid vehicles.

关键词： cranes optimisation road vehicles fuel economy mechanical engineering computing hydraulic systems hybrid vehicle control strategy fuel economy parallel hydraulic hybrid system global parameter matching AMESim simulation model optimisation algorithm EXCEL platform truck crane chassis

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SSO analysis and SSDC parameter optimisation based on the wind farm connected to HVDC transmission system

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JOURNAL OF ENGINEERING-JOE 2019年第16期2019卷 2170-2173页

作者： Xu, Yang Wang, Xitian Zhao, Dawei Qian, Minhui Yang, Bingdeng Liu, Shiyu Shanghai Jiao Tong Univ Inst Elect Engn Shanghai Peoples R China China Elect Power Res Inst Nanjing Nanjing Jiangsu Peoples R China

The interaction between renewable energy generation and high-voltage direct current (HVDC) transmission system becomes an essential cause of the subsynchronous oscillation (SSO) problem. In this study, the permanent magnet synchronous generator-based wind farm connected to HVDC transmission system is studied. The susceptivity analysis is carried out to find the main influence factors of the SSO problem. Besides, a parameter design method for subsynchronous damping controller is proposed. The configuration and the performance indicator are discussed, and then the optimisation algorithm is developed based on hybrid genetic algorithm and electromagnetic transient simulation. Finally, the proposed design method is validated by simulation in PSCAD/EMTDC.

关键词： synchronous generators HVDC power transmission damping power transmission control oscillations genetic algorithms HVDC power convertors permanent magnet generators wind power plants HVDC transmission system renewable energy generation high-voltage direct current transmission system essential cause subsynchronous oscillation problem permanent magnet synchronous generator-based wind farm susceptivity analysis main influence factors SSO problem parameter design method subsynchronous damping controller optimisation algorithm

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Non-contact voltage measurement of three-phase overhead transmission line based on electric field inverse calculation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第12期12卷 2952-2957页

作者： Xiao, Dongping Xie, Yutong Ma, Qichao Zheng, Qi Zhang, Zhanlong Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se 174 Shapingba Dist Chongqing Peoples R China

With the development of smart grid, the demand for voltage measurement along the overhead transmission lines is increasing. However, the installation of voltage transformers in the existing lines entails numerous difficulties. This study proposes a new non-contact measurement method by inversely calculating the voltages on AC overhead transmission lines based on the power-frequency electric field measurement data. To improve the calculation accuracy, the 3D model of transmission lines is built, and the relations between the 3D electric fields and voltages are presented. An improved algorithm that intermingles with the particle swarm algorithm and genetic algorithm is developed to ascertain optimal inverse solutions, meanwhile to improve the convergence speed and calculation stability. To further reduce the computational complexity, the constraint relations between the voltages and electric fields are derived, thereby the simplification from three decision variables to one is achieved. Then, some simulation cases with different measurement errors and voltage running states are conducted to show the good robustness and high accuracy of the proposed inversion method. Finally, a three-phase experimental system is built and the actual measured data are used to inversely calculate, which verifies the practicability of the proposed non-contact voltage measurement method.

关键词： voltage measurement power overhead lines inverse problems electric fields smart power grids potential transformers particle swarm optimisation computational complexity three-phase overhead transmission line electric field inverse calculation smart grid development voltage parameter measurement voltage transformers AC overhead transmission lines inverse calculation electric field measurement data 3D mathematical model particle swarm optimization algorithm genetic algorithm optimal inverse solutions convergence speed computational complexity optimisation algorithm multiple decision variables single decision variable selection strategy asymmetrical simulation three-phase symmetrical simulation measurement error 3D real-time electric field measurement devices noncontact voltage measurement method

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Estimation of solar radiation on PV panel surface with optimum tilt angle using vortex search algorithm

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IET RENEWABLE POWER GENERATION 2018年第10期12卷 1138-1145页

作者： Ramli, Makbul A. M. Bouchekara, Houssem R. E. H. King Abdulaziz Univ Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Hafr Al Batin Univ Dept Elect Engn Hafar al Batin 31991 Saudi Arabia

The amount of solar energy incidence on a photovoltaic (PV) panel depends on the PV tilt angles with respect to the horizon. It is thus crucial to investigate the optimum tilt angles to maximise the efficiency of PV panels and at the same time to increase the performance of solar energy systems. The objective of this study is to estimate the optimum tilt angle for PV panels in order to collect the maximum solar radiation for the city of Dhahran in Saudi Arabia. A newly developed optimisation algorithm called the vortex search algorithm is used to estimate the solar radiation on the tilted surface. Moreover, one year can be divided into different periods in the proposed approach, and the optimum angle can be obtained for each one of these periods separately. The horizontal solar data (i.e. direct, diffuse and global solar radiation) is used to estimate the optimum tilt angle. The results demonstrate that the solar radiation estimated using the optimum tilt angle is maximised compared with the one estimated on a horizontal surface.

关键词： solar cell arrays solar power search problems optimisation solar radiation estimation PV panel surface optimum tilt angle estimation vortex search algorithm solar energy photovoltaic panel Dhahran city Saudi Arabia optimisation algorithm horizontal solar data

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Non-linear system modelling based on NARX model expansion on Laguerre orthonormal bases

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IET SIGNAL PROCESSING 2018年第2期12卷 228-241页

作者： Benabdelwahed, Imen Mbarek, Abdelkader Bouzrara, Kais Garna, Tarek Univ Monastir Natl Sch Engineers Monastir Res Lab Automat Signal & Image Proc Ave Avicenne Monastir 5019 Tunisia

This study proposes a new representation of discrete Non-linear AutoRegressive with eXogenous inputs (NARX) model by developing its coefficients associated to the input, the output, the crossed product, the exogenous product and the autoregressive product on five independent Laguerre orthonormal bases. The resulting model, entitled NARX-Laguerre, ensures a significant parameter number reduction with respect to the NARX model. However, this reduction is still subject to an optimal choice of the Laguerre poles defining the five Laguerre bases. Therefore, the authors propose to use the genetic algorithm to optimise the NARX-Laguerre poles, based on the minimisation of the normalised mean square error. The performances of the resulting NARX-Laguerre model and the proposed optimisation algorithm are validated by numerical simulations and tested on the benchmark Continuous Stirred Tank Reactor.

关键词： nonlinear systems autoregressive processes mean square error methods minimisation genetic algorithms poles and zeros discrete time systems nonlinear system modelling NARX model expansion Laguerre orthonormal bases discrete nonlinear autoregressive with exogenous inputs model crossed product autoregressive product exogenous product parameter number reduction genetic algorithm NARX-Laguerre poles normalised mean square error minimisation optimisation algorithm numerical simulations benchmark continuous stirred tank reactor nonlinear discrete time system

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