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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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Feeder reconfiguration and capacitor allocation in the presence of non-linear loads using new P-PSO algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第10期10卷 2316-2326页

作者： Sayadi, Fahimeh Esmaeili, Saeid Keynia, Farshid Kerman Grad Univ Technol Dept Energy Kerman Iran Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran Grad Univ Adv Technol Dept Energy Kerman Iran

This study proposes a new particle swarm optimisation algorithm for combined problem of capacitor placement and network reconfiguration simultaneously in the presence of non-linear loads. Here, the minimising cost of real power losses and capacitor installation and also improving the power quality criteria have been pursued as the goals of an optimisation problem. In the proposed method, to achieve better control on the algorithm's exploration and exploitation capabilities, particles velocity will be dependent upon both particle's fitness and time. Perturbation module is adopted to perform perturbation on some particles and provide extra diversity to jump out from local optima and avoid premature convergence. The proposed model is implemented on two typical networks including 33-bus IEEE standard well-known test system and a 77-bus radial distribution network of Sirjan, Iran. Through showing numerical results, the performance of the presented method will be discussed in comparison to previously proposed ones. The numeric comparison also indicates that simultaneously capacitor placement and network reconfiguration lead to far better results than they are considered non-simultaneously. Furthermore, in regard to harmonic distortion, as a term of a multi-objective function, it also improves the power quality of the network during the reconfiguration and capacitor placement procedures.

关键词： particle swarm optimisation power distribution economics harmonic distortion power supply quality losses feeder reconfiguration capacitor allocation nonlinear loads P-PSO algorithm particle swarm optimisation algorithm capacitor placement network reconfiguration cost minimization power losses capacitor installation power quality criteria perturbation module 77-bus radial distribution network Sirjan Iran harmonic distortion multiobjective function

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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 operation of power sources of a PV/battery/hydrogen-powered hybrid charging station for electric and fuel cell vehicles

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IET RENEWABLE POWER GENERATION 2019年第16期13卷 3022-3032页

作者： Garcia-Trivino, Pablo Torreglosa, Juan P. Jurado, Francisco Fernandez Ramirez, Luis M. Univ Cadiz Dept Elect Engn Res Grp Elect Technol Sustainable & Renewable Ene EPS Algeciras Avda Ramon Puyol S-N Cadiz 11202 Spain Univ Huelva Elect Engn Dept Carretera Palos Huelva S-N Huelva 21071 Spain Univ Jaen Dept Elect Engn Res Grp Res & Elect Technol PAIDI TEP 152 EPS Linares Ave Univ Jaen 23700 Spain

This study presents a new energy management system (EMS) for the optimised operation of power sources of a hybrid charging station for electric vehicles and fuel cell vehicles. It is composed of a photovoltaic (PV) system, a battery and a hydrogen system as energy storage systems (ESSs), a grid connection, six fast charging units and a hydrogen supplier. The proposed EMS is designed to reduce the utilisation costs of the ESS and make them work, as much as possible, around their maximum efficiency points. The optimisation function depends on a cost prediction system that calculates the net present cost of the components from their previous performance and a fuzzy logic system designed for improving their efficiency. Finally, a particle swarm optimisation algorithm is used to solve the optimisation function and obtain the required power for each ESS. The proposed EMS is checked under Simulink environment for long-term simulations (25 years). By comparing the EMS with a simpler one that optimises only the costs, it is proved that the proposed EMS achieves better efficiency of the charging station (+7.35%) and a notable reduction in the loss of power supply probability (-57.32%) without compromising excessively its average utilisation cost (+1.81%).

关键词： electric vehicles particle swarm optimisation fuel cell vehicles energy storage battery powered vehicles power grids energy management systems optimisation distributed power generation battery storage plants hybrid power systems photovoltaic power systems energy storage systems local grid fast charging units rated power hydrogen supplier EMS utilisation costs ESS maximum efficiency points optimisation function cost prediction system net present cost fuzzy logic system particle swarm optimisation algorithm hybrid charging station power supply probability average utilisation cost optimised operation power sources cell vehicles energy management system electric vehicles photovoltaic system hydrogen system power 300 0 kW time 25 0 year

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Modelling of supercapacitors based on SVM and PSO algorithms

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IET ELECTRIC POWER APPLICATIONS 2018年第4期12卷 502-507页

作者： Ding, Shichuan Hang, Jun Wei, Baolei Wang, Qunjing Anhui Univ Sch Elect Engn & Automat Hefei Anhui Peoples R China Anhui Univ Natl Engn Lab Energy Saving Motor & Control Tech Hefei Anhui Peoples R China

Supercapacitors present an attractive energy storage alternative for high-performance applications due to their compact size and high-power density. Therefore, the supercapacitors have broad prospects for the development in the field of electric vehicles and renewable energy. To describe the output characteristics of the supercapacitors with high accuracy for the simulation research and practical application, a dynamic modelling method is proposed for the supercapacitors based on support vector machine (SVM) and particle swarm optimisation (PSO) algorithm. In this study, the SVM is used to predict the output voltage of the supercapacitors with the key parameters (temperature, current and initial voltage). The PSO algorithm is adopted to optimise the parameters of the SVM to improve the performance of the dynamic modelling. An experimental platform is established, where an electric machine drive system powered by the supercapacitors is controlled to operate at frequent acceleration and deceleration modes, thus leading to the frequent charging and discharging of the supercapacitors. The experimental data is collected to validate the effectiveness of the proposed method. The results show that the proposed method can effectively predict the output voltage of the supercapacitors.

关键词： supercapacitors particle swarm optimisation support vector machines power engineering computing electric drives machine control supercapacitors modelling SVM algorithm PSO algorithm energy storage dynamic modelling method support vector machine algorithm particle swarm optimisation algorithm output voltage prediction temperature current initial voltage parameter optimization electric machine drive system frequent acceleration mode frequent mode frequent supercapacitor charging frequent supercapacitor discharging

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Optimal pricing and inventory strategies for leased equipment considering lessees' options

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第19期62卷 7261-7278页

作者： Liu, Yanping Liu, Biyu Yang, Haidong Luo, Kai Fuzhou Univ Sch Econ & Management Fuzhou Peoples R China Fuzhou Univ Logist Res Ctr Fujian Social Sci Res Base Fuzhou Peoples R China Paris Sch Business Paris France Fuzhou Univ Fuzhou Univ Town Sch Econ & Management 2 Wulong Jiangbei Ave Fuzhou Fujian Peoples R China

After the expiration of each lease, lessees may return leased equipment on schedule, renew the lease or purchase it according to the equipment status and their demands. By considering lessees' uncertain options and equipment status difference, the pricing and inventory decision-makings of leased equipment are explored. A mixed-integer nonlinear programming model by maximising the net present value of lessor's profit is presented with respect to constraints like rental revenue, manufacturing, lessees' credit check, transportation, maintenance, upgrade and inventory costs. The rental price and inventory decisions are obtained by solving the problem with a particle swarm optimisation algorithm. We also analyse the impacts of purchasing cost of old equipment from a third-party supplier on lessor's inventory, renewal price or purchasing price of leased equipment on lessees' options and lessor's profit. The results show: (1) with the extension of lease period, the rental price increases while the growth rate decreases;(2) the maintenance cost accounts for about 20% of total cost, and the preventive maintenance strategy can reduce excess maintenance cost as lease period increases;(3) the lessor shall set moderate renewal price discount coefficient and purchasing price coefficient, and analyse purchasing cost of old equipment to manage inventory timely.

关键词： Pricing and inventory leasing lessees' options preventive maintenance particle swarm optimisation algorithm

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Generalised model predictive controller design for A DC-DC non-inverting buck-boost converter optimised with a novel identification technique

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IET POWER ELECTRONICS 2022年第13期15卷 1350-1364页

作者： Ghamari, Seyyed Morteza Khavari, Fatemeh Molaee, Hasan Wheeler, Patrick Shahrood Univ Technol Semnan Fac Elect & Comp Engn Semnan Iran Univ Nottingham Fac Engn Nottingham England

An on-line generalised model predictive control (GMPC) strategy is designed and optimised with a novel identification procedure in the presence of different disturbances. The principle of MPC is utilising a discrete-time model of a system to reach the control variables with a prediction over these values, which is followed by computing a cost function for the control aims. Non-inverting buck-boost converter is a non-minimum phase system based on its boost mode, which makes a challenging condition for designing a stable controller. The proposed control technique described in this paper removes the requirement for a system mathematical model adopting a black-box identification method which can decrease the computational burden. Numerous harmful disturbances can affect a DC-DC converter;thus, the GMPC scheme is used along with a novel improved exponential regressive least identification algorithm as an adaptive strategy for the controller to optimise the gains of the controller in an on-line way resulting in better disturbance rejection. A PID controller with particle swarm optimisation algorithm is designed for this converter to be compared with the GMPC controller. Finally, the efficiency of the GMPC is verified in various performances with experimental and simulation results.

关键词： generalised model predictive controller design controller design stability three-term control discrete-time model black-box identification method nonminimum phase system predictive control DC-DC noninverting buck-boost converter DC-DC power convertors control system synthesis particle swarm optimisation identification DC-DC converter GMPC controller design exponential regressive least identification algorithm power system control particle swarm optimisation algorithm system mathematical model PID controller regression analysis

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Soft computing applied to the build of textile defects inspection system

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IET COMPUTER VISION 2013年第5期7卷 373-381页

作者： Darwish, Saad Mohamed Univ Alexandria Dept Informat Technol Inst Grad Studies & Res Alexandria 21526 Egypt

The inspection of textile defects is challenging because of the large number of defects categories that are characterised by their imprecision and uncertainty. In this study, novel interval type-2 fuzzy system is proposed for resolving defects recognition problem of textile industries. The proposed system mixes interval type-2 fuzzy reasoning and swarm optimisation algorithm together in order to enhance the defects classification capabilities. Interval type-2 fuzzy logic is powerful in handling high level of indecisions in the human decision making process, including uncertainties in measurements of textile features and data used to calibrate the examination's parameters. swarm intelligence algorithm is used to optimise parameters of the membership functions to increase the accuracy of fuzzy controller. Besides, the problem of fuzzy linguistic rules learning has been tackled by utilising ant colony meta-heuristic method to reduce the complexity of the inspection system. Excellent recogniser results on real textile samples, using this system, are demonstrated.

关键词： decision making fuzzy logic fuzzy reasoning heuristic programming inspection learning (artificial intelligence) particle swarm optimisation production engineering computing textile industry ant colony optimisation soft computing textile defect inspection system defects recognition problem textile industries interval type-2 fuzzy reasoning system particle swarm optimisation algorithm defects classification capability interval type-2 fuzzy logic human decision making process examination parameter calibration swarm intelligence algorithm membership functions fuzzy controller fuzzy linguistic rules learning ant colony metaheuristic method

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Pattern synthesis for multi-feed reflector antennas using invasive weed optimisation

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IET MICROWAVES ANTENNAS & PROPAGATION 2012年第14期6卷 1583-1589页

作者： Foudazi, A. Mallahzadeh, A. R. Shahed Univ Elect & Elect Engn Dept Tehran Iran

The authors present a new optimisation method for pattern synthesis of multi-feed reflector antennas in this study. By using an invasive weed optimisation (IWO) algorithm, shaped beam antennas are designed. The results of the IWO algorithm for the synthesis of cosecant squared and contoured beam patterns are presented and compared with particle swarm optimisation algorithm results. The far-field radiation patterns of reflector antennas, fed by linear and planar array of conical horn antennas, are provided by a physical optics and the error between desired and provided radiation patterns is calculated by a root mean square method. The results show that the IWO algorithm, which provides good convergence, is stable and adaptive-to-change for different boundary conditions in pattern synthesis applications.

关键词： conical horn antennas particle swarm optimisation optimisation techniques contoured beam pattern synthesis IWO algorithm horn antennas Antenna accessories planar antenna arrays physical optics particle swarm optimisation algorithm conical antennas far-field radiation patterns planar antenna array mean square error methods shaped beam antennas Antenna arrays invasive weed optimisation method antenna radiation patterns Interpolation and function approximation (numerical analysis) cosecant squared pattern synthesis root mean square method reflector antenna feeds linear antenna array multifeed reflector antennas linear antenna arrays

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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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