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Hybrid precoding for millimetre wave MIMO systems based on particle swarm optimisation

IET COMMUNICATIONS 2019年第11期13卷 1643-1650页

作者： Cui, Mingyang Zou, Weixia Wang, Ye Zhang, Ran Beijing Univ Posts & Telecommun MOE Key Lab Univ Wireless Commun Beijing 100876 Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Jiangsu Peoples R China

Millimetre wave (mmWave) multiple-input multiple-output (MIMO) systems have been proposed to enable Gbps communication for next-generation cellular systems and local area networks. To compensate for the severe propagation loss of the mmWave channel, a cost-effective hybrid precoding architecture, combining a digital precoder and an analogue precoder, is widely used in mmWave MIMO systems. In this study, two hybrid precoding algorithms based on particle swarm optimisation (PSO) algorithm will be proposed for two different hybrid precoding structures, i.e. the fully connected and partially connected structures. First, the authors use the phase of the analogue precoding variable to replace the corresponding variable with the constraints of the unit norm to solve the non-convex constraints skillfully. Then they design analogue precoding by using PSO algorithm and compute the digital precoding based on the least squares solution. Finally, the proposed algorithms are compared with the existing advanced algorithms. Simulation results demonstrate that the proposed algorithms can approach the optimal performance under the corresponding structure.

关键词： particle swarm optimisation precoding MIMO communication channel coding concave programming millimetre wave communication cellular radio local area networks wireless channels digital precoder analogue precoder mmWave MIMO systems hybrid precoding algorithms particle swarm optimisation algorithm fully connected structures partially connected structures analogue precoding PSO algorithm digital precoding millimetre wave MIMO systems millimetre wave multiple-input multiple-output systems next-generation cellular systems local area networks severe propagation loss mmWave channel cost-effective hybrid precoding architecture hybrid precoding structures least squares solution nonconvex constraints

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particle swarm optimisation-based model and analysis of photovoltaic module characteristics in snowy conditions

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IET RENEWABLE POWER GENERATION 2019年第11期13卷 1950-1957页

作者： Khenar, Mohammad Hosseini, Seyedkazem Taheri, Shamsodin Cretu, Ana-Maria Pouresmaeil, Edris Taheri, Hamed Univ Quebec Outaouais Dept Comp Sci & Engn 101 Rue St Jean Bosco Gatineau PQ J8X 3X7 Canada Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada Aalto Univ Dept Elect Engn & Automat Espoo 00076 Finland Primax Technol Inc 65 Hymus Blvd Montreal PQ H9R 1E2 Canada

In this study, a novel methodology of photovoltaic (PV) modelling is proposed to represent the instantaneous electrical characteristics of PV modules covered with snow. The attenuation of the transmitted solar radiation penetrating a layer of snow is rigorously estimated based on the Giddings and LaChapelle theory. This theory introduced the level of radiation that reaches the surface of the PV module through the snowpack, significantly affected by the snow properties and thickness. The proposed modelling approach is based on the single-diode-five-parameter equivalent circuit model. The parameters of the model are updated through instantaneous measurements of voltage and current that are optimised by the particle swarm optimisation algorithm. The proposed approach for modelling snow-covered PV modules was successfully validated in outdoor tests using three different types of PV module technologies typically used in North America's PV farms under different cold weather conditions. In addition, the validity of the proposed model was investigated using real data obtained from the SCADA system of a 12-MW grid-connected PV farm. The proposed model can help to improve PV performance under snow conditions and can be considered a powerful tool for the design and selection of PV modules subjected to snow accretion.

关键词： solar cells equivalent circuits power grids particle swarm optimisation SCADA systems photovoltaic power systems snow particle swarm optimisation-based model photovoltaic module characteristics snowy conditions photovoltaic modelling instantaneous electrical characteristics transmitted solar radiation snow properties thickness modelling approach single-diode-five-parameter equivalent circuit model particle swarm optimisation algorithm snow-covered PV modules PV module technologies North America's PV farms different cold weather conditions 12-MW grid-connected PV farm PV performance snow conditions snow accretion

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Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi-speed scheduler

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第10期14卷 1200-1209页

作者： Wahid, Nurbaiti Zamzuri, Hairi Amer, Noor H. Dwijotomo, Abdurahman Saruchi, Sarah Atifah Mazlan, Saiful Amri Univ Teknol MARA Fac Elect Engn Terengganu 23000 Malaysia Univ Teknol Malaysia Malaysia Japan Int Inst Technol Kuala Lumpur 54100 Malaysia eMoovit Technol Sdn Bhd Futurise Ctr Prime Lab 3Level 1Cyber 8 Cyberjaya 63000 Selangor Malaysia Univ Pertahanan Nas Malaysia Dept Mech Engn Kuala Lumpur 57000 Malaysia

This study presents an adaptive motion planning strategy for automated vehicle collision avoidance systems to be associated with the variation of collision speed region based on the position of the obstacle. This is done by designing the motion planner using an artificial potential field (APF) with the incorporation of an adaptive multi-speed scheduler using fuzzy system in the motion planning structure. The knowledge database information is developed based on the risk perception of the driver that consists of APF parameters and was optimised by using particle swarm optimisation algorithm. This study contributes to the improvement of a feasible reference motion generated by the motion planner that can be converted into desired control signals. The reference motion resulted to provide the control command that managed to avoid collision successfully by evasive manoeuvre without lane departure when adapting to variation in the vehicle speeds with different obstacle positions. The results indicated the reduction of the lateral error with respect to the reference avoidance trajectory data of up to 87% compared to base-type APF with maximum reference lateral motion is reduced of up to 26%. Then, a hardware-in-loop test is conducted to verify the proposed strategy using a steering wheel system.

关键词： mobile robots particle swarm optimisation motion control position control collision avoidance adaptive multispeed scheduler adaptive motion planning strategy automated vehicle collision avoidance systems collision speed region motion planner artificial potential field fuzzy system motion planning structure knowledge database information APF parameters particle swarm optimisation algorithm feasible reference motion obstacle positions reference avoidance trajectory data base-type APF maximum reference lateral motion vehicle collision avoidance motion planning strategy

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High-accuracy localisation method for PD in transformers

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2020年第1期14卷 104-110页

作者： Cai, Junyi Zhou, Lijun Hu, Junjie Zhang, Chenqingyu Liao, Wei Guo, Lei Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China

This study proposes a high-accuracy method of localising partial discharge (PD) for transformer fault diagnosis. This study aims to solve the problem of high-accuracy estimation of PD in transformers by detecting the acoustic signals. First, by combining the advantages of the differential evolution (DE) algorithm and the particle swarm optimisation (PSO) algorithm, the authors describe a hybrid DE-PSO algorithm that can maintain great diversity even at the later stage of calculation. For further accuracy, a cooperative localisation differential evolution-particle swarm optimization-correction-Newton's method (DPCN) algorithm based on the DE-PSO algorithm and Newton's method with consideration of corrected time difference of arrival values is proposed. The results of simulations and experiments show that the proposed algorithm has excellent performance with high accuracy and strong robustness, and it can meet the needs of field applications.

关键词： fault diagnosis particle swarm optimisation partial discharges evolutionary computation transformers Newton method partial discharge PD transformer fault diagnosis high-accuracy estimation transformers acoustic signals differential evolution particle swarm optimisation algorithm hybrid DE-PSO algorithm localisation algorithm Newton's method high-accuracy localisation method high-accuracy method

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Robust calibration method for distributed ISAR time-varying frequency errors based on the contrast maximisation principle

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IET RADAR SONAR AND NAVIGATION 2020年第7期14卷 1068-1078页

作者： Kang, Hailong Li, Jun Zhang, Zijing Ma, Hui Li, Han Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

The linear time-varying frequency errors (LTFE) caused by the mismatch of transmitter and receiver oscillators can defocus the imaging result of distributed inverse synthetic aperture radar (ISAR) seriously. The LTFE calibration method based on the entropy minimization principle is sensitive to signal-to-noise ratio (SNR), and its performance is degraded significantly under low SNR conditions. In addition, this method uses enumeration algorithm to solve the optimization problem, which has a heavy computation burden. Therefore, a robust calibration method based on the contrast maximization principle is proposed. Compared with image entropy, image contrast has better anti-noise ability because it has better sensitivity property, namely, the change of image contrast is sharper than the change of image entropy. In the proposed method, the estimation of frequency error coefficient is modelled as an unconstrained optimization problem with image contrast as cost function, and the particle swarm optimization (PSO) algorithm is used to search the global optimal solution. Then, the LTFE can be calibrated by the estimated frequency error coefficient. The proposed method has better robustness, which can work well under low SNR conditions. Besides, it has higher computational efficiency. Simulations are carried out to verify the effectiveness and robustness of the proposed method.

关键词： radar imaging synthetic aperture radar entropy minimisation optimisation calibration particle swarm optimisation robust calibration method distributed ISAR time-varying frequency errors contrast maximisation principle linear time-varying frequency errors transmitter receiver oscillators imaging result distributed inverse synthetic aperture radar LTFE calibration method entropy minimisation principle signal-to-noise ratio low SNR conditions heavy computation burden distributed ISAR LTFE image entropy image contrast anti-noise ability unconstrained optimisation problem particle swarm optimisation algorithm estimated frequency error coefficient

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Optimal optimisation-based microgrid scheduling considering impacts of unexpected forecast errors due to the uncertainty of renewable generation and loads fluctuation

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IET RENEWABLE POWER GENERATION 2020年第2期14卷 321-331页

作者： Lee, Chun-Yao Tuegeh, Maickel Chung Yuan Christian Univ Dept Elect Engn 200 Zhongbei Rd Taoyuan 320 Taiwan

Unlike conventional power plants, wind farm and solar photovoltaic (PV) module operations involved uncontrollable factors such as wind and solar irradiation, making the dispatch problem more complex. This study proposes optimal scheduling using a modification inertia weight of the particle swarm optimisation (PSO) algorithm and take into account unexpected forecast errors due to uncertainty in renewable distributed generators and loads in the day-ahead market in a microgrid. Modified inertia weight makes the PSO algorithm have a strong global searching ability to solve the scheduling. Meanwhile, the microgrid coordinates the realistic production of its power plant to get the optimal total cost. The uncertainty of renewable distributed generators is modelled based on forecast data. The Simulation result shows the advantages of the proposed method.

关键词： power markets scheduling particle swarm optimisation optimisation search problems wind power plants distributed power generation power generation scheduling conventional power plants renewable generation optimal optimisation-based microgrid scheduling forecast data optimal total cost power plant strong global searching ability PSO algorithm modified inertia weight day-ahead market renewable distributed generators account unexpected forecast errors particle swarm optimisation algorithm modification inertia weight optimal scheduling dispatch problem solar irradiation uncontrollable factors solar photovoltaic module operations wind farm

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Optimised PI-4VPI current controller for three-phase grid-integrated photovoltaic inverter under grid voltage distortions

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IET RENEWABLE POWER GENERATION 2020年第5期14卷 779-792页

作者： Patel, Nirav Kumar, Ajay Gupta, Nitin Ray, Soumyadeep Babu, B. Chitti Malaviya Natl Inst Technol Jaipur Dept Elect Engn JLN Marg Jaipur 302017 Rajasthan India KPIT Technol Pune Maharashtra India Indian Inst Informat Technol Design & Mfg Kanchee Dept Elect & Commun Engn Chennai Tamil Nadu India

This study aims to propose optimised and robust current controller for the transformerless grid-integrated photovoltaic inverter. Challenge of ensuring improved power quality while minimising the DC-component under non-ideal grid condition is at the forefront in the photovoltaic system. In this context, an advanced current controller is designed and developed in the stationary reference frame with proportional-integral (PI) plus four-vector PI (4VPI) controllers. The PI-controller modulates the shape of fundamental current to follow its reference induced from outer DC-link loop. Meanwhile, 4VPI controllers are tuned at the multiple of grid frequencies including third-(3 omega(0)), fifth-(5 omega(0)), seventh-(7 omega(0)), and ninth-(9 omega(0)) to introduce infinite gain and thereby alleviating stationary errors in the current loop of the photovoltaic inverter. The most distinguishing feature of the proposed controller is its inherent control robustness, and therefore, it operates satisfactorily against control delay time and frequency excursion. Traditionally, cumbersome trial-and-error techniques have been largely employed to adjust the PI-VPI controller gains. Nonetheless, tuning of these parameters has made many practitioners switch-off from employing the proposed controller. Given this, the particle swarm optimisation algorithm is proposed. Finally, the practicality of the proposed controller is verified through comprehensive set of simulation and experimental tests conducted on a laboratory-developed prototype using dSPACE-1104.

关键词： robust control power grids voltage control invertors photovoltaic power systems electric current control particle swarm optimisation PI control power supply quality power generation control optimal control four-vector PI controllers grid frequencies control robustness control delay time PI-VPI controller gains three-phase grid-integrated photovoltaic inverter grid voltage distortions optimised controller robust current controller transformerless grid-integrated photovoltaic inverter nonideal grid condition grid-integrated photovoltaic system optimised PI-4VPI current controller power quality DC-component proportional–integral control DC-link loop particle swarm optimisation algorithm

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Fast and systematic approach for adjusting ROCOF relay used in islanding detection of SDG

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第2期14卷 275-283页

作者： Ahmadzadeh-Shooshtari, Babak Hamedani Golshan, Mohamad Esmail Rezaei-Zare, Afshin York Univ Dept Elect Engn & Comp Sci Lassonde Sch Engn Toronto ON M3J 1P3 Canada Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

Frequency-based relays, commonly used for islanding detection of synchronous distributed generation (SDG), may either have large non-detection zones (NDZs) or wrongly operate for non-islanding disturbances unless they are efficiently adjusted. Trial and error simulation-based methods, employing application region (AR) and/or power imbalance AR (PIAR), are presented in the literature to adjust these relays for quick islanding detection without false trips. However, the associated settings are not optimal and finding them is time-consuming. In this study, an approach utilising particle swarm optimisation algorithm is proposed to adjust a rate of change of frequency (ROCOF) relay, one of the most effective relays in SDG islanding detection. The proposed approach needs a set of analytical formulas for determining the ROCOF relay performance curve and NDZ as well as AR and PIAR. These formulas are developed to be employed in the relay adjustment. By applying the developed analytical formulas to the optimisation-based approach, the ROCOF relay is adjusted fast and systematically. It is shown that while the performance of the relay adjusted by the proposed approach is close to the one with setting obtained by corresponding trial and error-based method, the latter is much more time-consuming than the former.

关键词： relay protection distributed power generation power distribution faults power distribution protection particle swarm optimisation systematic approach frequency-based relays synchronous distributed generation nondetection zones nonislanding disturbances error simulation-based methods quick islanding detection associated settings particle swarm optimisation algorithm frequency relay effective relays SDG islanding detection ROCOF relay performance curve optimisation-based approach error-based method ROCOF relay adjustment

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Siting and sizing of electric vehicle fast-charging station based on quasi-dynamic traffic flow

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IET RENEWABLE POWER GENERATION 2020年第19期14卷 4204-4215页

作者： Qian, Zhang Yi, Zhu Zhong, Wang Yue, Hu Yaojia, Su Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se 174 Shazhengjie Chongqing Peoples R China State Grid Chengdu Power Supply Co 63Sect 4Renmin South Rd Chengdu Sichuan Peoples R China

In engineering practice, the Voronoi diagram is often used to plan electric vehicle (EV) fast-charging stations but it does not consider the spatio-temporal distribution of EV trip. Research on the spatio-temporal distribution of EVs mainly uses the shortest path method to reduce the amount of iterative calculations. The influence of the external environment and traffic congestion on the travel of EVs has not been considered, which results in the inaccuracy of the EV charging demand. To solve the above problems, this study proposes a method for predicting the spatio-temporal distribution of EVs based on quasi-dynamic traffic flow. This method takes into account the external environment and the impact of traffic congestions on EV trips and balances the problem between simulation accuracy and calculation efficiency. Based on this, the particle swarm optimisation algorithm is used to optimise the travel cost of the EVs and the cost of the construction and operation of the charging infrastructure. An optimal siting and sizing model for the fast-charging station based on quasi-dynamic traffic flow is established. Simulation results verify the effectiveness of the model.

关键词： particle swarm optimisation electric vehicles electric vehicle charging iterative methods computational geometry road traffic particle swarm optimisation algorithm iterative calculations Voronoi diagram sizing model optimal siting charging infrastructure EV charging demand shortest path method spatio-temporal distribution electric vehicle fast-charging stations quasidynamic traffic flow

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FHCS: Hybridised optimisation for virtual machine migration and task scheduling in cloud data center

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IET COMMUNICATIONS 2020年第12期14卷 1942-1948页

作者： Balaji Naik, Banavath Singh, Dhananjay Samaddar, Arun B. Natl Inst Technol Sikkim Dept Comp Sci & Engn Sikkim India HUFS Global Campus Dept Elect Engn Yongin South Korea Natl Inst Technol Sikkim Dept Comp Sci & Engn Sikkim India

Cloud computing and virtualisation are recent approaches to develop minimum energy usage in virtualised cloud data centre (DC) for resource management. One of the major problems faced by cloud DCs is energy consumption which increases the cost of cloud user and environmental influence. Therefore, virtual machine (VM) consolidation is properly proposed in many approaches which reallocate the VMs by VM migration with the objective of minimum energy consumption. Here, VM consolidation based on the Fruit fly Hybridised Cuckoo Search (FHCS) algorithm is proposed to obtain the optimal solution with the help of two objective functions in cloud DC. This FHCS approach efficiently minimises the energy usage and resource depletion in cloud DC. The proposed work comparison is done with Ant Colony System (ACS), particle swarm optimisation (PSO) algorithm and Genetic algorithm (GA). The simulation conclusion reveals the advantage of the FHCS and VM migration method over existing procedures such as GA, PSO and ACS in terms of energy consumption and resource utilisation. The proposed method achieves 68 Kwh less energy and 72% less resources than existing methods. Simulation results have shown that energy consumption of the proposed method is reduced with less number of active PMs than other conventional approaches.

关键词： computer centres cloud computing virtual machines particle swarm optimisation genetic algorithms virtualisation energy consumption search problems virtual machine migration task scheduling cloud data center minimum energy usage virtualised cloud data centre resource management cloud DC cloud user environmental influence virtual machine consolidation minimum energy consumption VM consolidation objective functions FHCS approach resource depletion particle swarm optimisation algorithm Genetic algorithm VM migration method resource utilisation hybridised optimisation Fruit fly Hybridised Cuckoo Search algorithm

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