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Improved strategy of particle swarm optimisation algorithm for reactive power optimisation

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2010年第1期2卷 27-33页

作者： Lu, Jin-gui Zhang, Li Yang, Hong Du, Jie Nanjing Univ Technol Ctr CAD Nanjing 210009 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Dept Comp Nanjing Jiangsu Peoples R China

This paper involves the application of particle swarm optimisation in the optimisation problem of reactive power. The optimisation model of reactive power is first introduced and the improved strategy of particle swarm optimisation is proposed for the problem of optimisation of reactive power in this paper. In order to improve the local search ability, the disturbance item is given for the updating equation of the particle in the improved strategy. The numerical examples of standard IEEE-6 and IEEE-30 power systems for the improved strategy of particle swarm optimisation are performed for the reactive power optimisation. The effectiveness of the improved strategy proposed in this paper has been demonstrated preliminarily from the numerical examples.

关键词： reactive power optimisation RPO power flow calculation particle swarm optimisation algorithm PSO

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Focusing inverse synthetic aperture radar images with higher-order motion error using the adaptive joint-time-frequency algorithm optimised with the genetic algorithm and the particle swarm optimisation algorithm - comparison and results

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IET SIGNAL PROCESSING 2010年第4期4卷 329-342页

作者： Brinkman, W. Thayaparan, T. Def Res & Dev Canada Dept Natl Def Ottawa ON Canada

algorithms based on the genetic algorithm (GA) and the particle swarm optimisation (PSO) algorithm were designed for focusing inverse synthetic aperture radar (ISAR) images that suffered from degradation because of Doppler smearing. These algorithms optimised the adaptive joint-time-frequency (AJTF) algorithm by replacing the exhaustive search as the primary search tool used to determine focusing parameters. The use of the PSO for ISAR image focusing is a unique application of this evolutionary search. Performance of the GA and the PSO were compared with the PSO producing the optimal results of being able to focus a 2(11) pulse ISAR image with second-order motion error in 9 s or 24% of the cost function calculations required for an exhaustive search. The PSO algorithm was then applied to a 2(11) pulse ISAR image with fourth-order motion error. The PSO algorithm was able to focus this image in 20 s with 33% of the cost function calculations required by the exhaustive search. This study also introduces a new method of determining basis function suitability using the fast Fourier transform.

关键词： fourth-order motion error ISAR image genetic algorithm particle swarm optimisation Integral transforms in numerical analysis adaptive joint-time-frequency algorithm fast Fourier transforms Optical, image and video signal processing higher-order motion error particle swarm optimisation algorithm inverse synthetic aperture radar images synthetic aperture radar radar imaging optimisation techniques Radar equipment, systems and applications genetic algorithms fast Fourier transform

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Exergetic optimisation of a multi-stage compression transcritical refrigeration cycle

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INTERNATIONAL JOURNAL OF EXERGY 2016年第1期20卷 22-47页

作者： Rashidi, M. M. Parsa, A. Basiri Shamekhi, L. Nazari, F. Ali, M. Tongji Univ Shanghai Key Lab Vehicle Aerodynam & Vehicle Ther 4800 Cao An Rd Shanghai 201804 Peoples R China ENN Tongji Clean Energy Inst Adv Studies Shanghai Peoples R China Islamic Azad Univ Young Researchers & Elite Club Hamedan Branch Hamadan *** Iran Univ Paris 10 LTIE GTE EA 4415 50 Rue Sevres F-92410 Ville Davray France King Saud Univ Dept Mech Engn Coll Engn POB 800 Riyadh 11421 Saudi Arabia

Thermodynamic analyses as well as optimisation studies based on maximum cooling coefficient of performance (COP) as thermal efficiency and optimum values of exergy efficiency and total input work of a three-stage compression transcritical CO2 cycle have been presented. A new triple-stage configuration of compression is presented, which uses two intercoolers to enhance the performance. For parametric optimisation, COP and total input work are selected as objective functions, which are calculated for different values of outlet pressure of first and second compressors. For optimisation, a procedure based on artificial neural network and particle swarm optimisation (PSO) algorithm is proposed. The procedure includes two stages. According to the parametric analysis data, in the first stage two different multilayer perceptron neural networks are trained. In the next stage, two distinct PSO algorithms are used to optimise total input work and thermal efficiency.

关键词： CO2 refrigerant COP coefficient of performance exergy efficiency Artificial neural network particle swarm optimisation algorithm

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Hybrid control of aggregated thermostatically controlled loads: step rule, parameter optimisation, parallel and cascade structures

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第16期10卷 4149-4157页

作者： Ma, Kai Yuan, Chenliang Liu, Zhixin Yang, Jie Guan, Xinping Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China Shanghai Jiao Tong Univ Dept Automat Key Lab Syst Control & Informat Proc Minist Educ Shanghai 200240 Peoples R China

This study proposes hybrid control strategies for aggregated thermostatically controlled loads (TCLs) in order to provide ancillary service. The on/off control strategy and the setpoint-regulation strategy are two typical control strategies. In order to reduce the tracking errors of the two control strategies, two improved methods were developed. The first method dispatches the aggregated loads to the automatic generation control signal following a step rule based on the state of charge. The second method optimises the controller parameters of the setpoint-regulation strategy by the Powell optimisation algorithm. Based on these two improved methods, the hybrid control strategies with parallel and cascade control structures were established by dividing the TCLs into two clusters, and the optimal allocations of the loads, the reference signal and the tracking error between the two clusters were obtained by the particle swarm optimisation algorithm. The simulation results demonstrate that the hybrid control strategies can reduce the errors in load following.

关键词： particle swarm optimisation load regulation controllers aggregated thermostatically controlled loads parameter optimisation cascade structures parallel structures hybrid control strategies thermostatically controlled loads setpoint-regulation strategy control strategies tracking errors control strategies automatic generation control signal controller parameters Powell optimisation algorithm particle swarm optimisation algorithm

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optimisation model for power system restoration with support from electric vehicles employing battery swapping

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第3期10卷 771-779页

作者： Sun, Lei Wang, Xiaolei Liu, Weijia Lin, Zhenzhi Wen, Fushuan Ang, Swee Peng Salam, Md. Abdus Zhejiang Univ Sch Elect Engn Hangzhou 310027 Zhejiang Peoples R China Inst Teknol Brunei Dept Elect & Elect Engn BE-1410 Bandar Seri Begawan Brunei

The energy stored in the batteries of electric vehicles (EVs) could be employed for starting generators when a blackout or a local outage occurs. Considering the feature of the battery swapping mode, an available capacity model of the batteries in a centralised charging station is first developed. Then, the authors analyse the start-up characteristics of a generator powered by batteries and propose a bi-level optimisation-based network reconfiguration model to determine the restoration paths with an objective of maximising the overall generation capability. In the upper-level optimisation model, the generator start-up sequence is optimised, whereas the restoration paths are optimised in the lower-level one. Moreover, they consider the uncertainties associated with the available capacity of the batteries. The bi-level optimisation model for the network reconfiguration is developed in the chance-constrained programming framework and solved by the well-established particle swarm optimisation algorithm. Finally, case studies are employed to demonstrate the effectiveness of the presented model. Simulation results show that a centralised EV charging station could act as a power source to effectively restore a power system without black-start (BS) generators or with insufficient cranking power from BS generators, and the presented model could be used to guide actual system restorations.

关键词： power system restoration battery powered vehicles secondary cells power generation reliability particle swarm optimisation electric vehicles power system restoration blackout EV battery swapping mode starting generator start-up characteristics centralised charging station outage bilevel optimisation-based network reconfiguration upper-level optimisation model chance-constrained programming framework particle swarm optimisation algorithm black start generator BS generator

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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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Including heavy vehicles in a car-following model: modelling, calibrating and validating

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JOURNAL OF ADVANCED TRANSPORTATION 2016年第7期50卷 1432-1446页

作者： Aghabayk, Kayvan Sarvi, Majid Young, William Univ Tehran Sch Civil Engn Coll Engn Tehran Iran Univ Melbourne Dept Infrastruct Engn Parkville Vic Australia Monash Univ Inst Transport Studies Dept Civil Engn Clayton Vic Australia

Heavy vehicles influence general traffic in many different ways compared with passenger vehicles, and this may result in different levels of traffic instability. Increases in the number and proportion of heavy vehicles in the traffic stream will therefore result in different traffic flow conditions. This research initially outlines the different car-following behaviour of drivers in congested heterogeneous traffic conditions indicating the necessity for developing a car-following model, which includes these differences. A psychophysical car-following model, similar in form to Weideman's car-following model, was developed. Due to the complexity of the developed model, the calibration of the model was undertaken using a particle swarm optimisation algorithm with the data recorded under congested traffic conditions. This was then incorporated into a traffic microsimulation model. The results showed that the car-following perceptual thresholds and thus action points of drivers differ based on their vehicle and the lead vehicle types. The inclusion of the heavy vehicles in the model showed significant impacts on the traffic dynamic and interactions amongst different vehicles. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： heavy vehicle car-following heterogeneous traffic traffic microsimulation auto calibration evolutionary algorithm particle swarm optimisation algorithm

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Frequency regulation of multi-area power systems with plug-in electric vehicles considering communication delays

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第14期10卷 3481-3491页

作者： Fan, Hua Jiang, Lin Zhang, Chuan-Ke Mao, Chengxiong Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England China Univ Geosci Sch Automat Wuhan 430074 Peoples R China

Dynamics of vehicle-to-grid service to frequency regulation may suffer from the uncertainties arising from the spatial and temporal diversities of the large-scale plug-in vehicles (PEVs). Communication delays incurred both in the aggregation of the PEVs and in load frequency control (LFC) loop also degrade the dynamics and even destabilise the system. This study designs the LFC scheme considering those uncertainties. First, a dynamic model of individual PEV based on equivalent circuit and an aggregated model for PEVs group considering driving behaviours, battery characteristics, and delays are developed. Then, a state-space model with time delays and uncertainties is constructed to model the closed-loop LFC scheme with PEVs in primary control loop. After constructing the relationships among control gains, robust performances, and delays, a robust proportional-integral-derivative-type LFC scheme design method is developed by using linear matrix inequality and particle swarm optimisation algorithm. Case studies based on a three-area LFC system demonstrate that the PEVs contribute to the frequency regulation under different load disturbances and that the proposed LFC scheme successfully suppresses frequency fluctuations in the presence of delays and provides robustness against the uncertainties arising from the PEVs.

关键词： frequency control electric vehicles load regulation closed loop systems equivalent circuits state-space methods delays three-term control linear matrix inequalities particle swarm optimisation frequency regulation multiarea power system plug-in electric vehicle communication delay vehicle-to-grid service PEV load frequency control closed-loop system Thevenin equivalent circuit battery characteristic state-space model time delay robust proportional-integral-derivative-type LFC scheme design linear matrix inequality technique particle swarm optimisation algorithm long-term random load demand frequency fluctuation suppression

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particle swarm optimisation algorithm for crack shape reconstruction in magnetic flux leakage nondestructive testing

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NONDESTRUCTIVE TESTING AND EVALUATION 2009年第1-2期24卷 243-250页

作者： Zhang, Yong Ye, Zhongfu Hefei Univ Technol Sch Comp & Informat Hefei Peoples R China Univ Sci & Technol China Inst Stat Signal Proc Hefei Peoples R China Hefei Univ Technol Sch Comp & Informat Hefei Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China

Magnetic flux leakage testing is widely used to examine ferromagnetic materials. Considering the importance of estimating the size of surface crack in metals, which is used in nuclear power, railway and piping industries, a method based on particle swarm optimisation algorithm is proposed in this article. This approach maps the size of crack to the location of particles. After initialising the locations and velocities of the particle population, it evaluates the fitness value of the individual particle and tracks the optimal one. It then updates the positions and velocities of particles with the previous fitness values and repeats these steps until the best possible result is obtained. The fitness value is obtained by summing the absolute measurement error of the magnetic leakage field intensity of the crack. The results of simulation experiments demonstrate the effectiveness of the proposed method.

关键词： magnetic flux leakage crack shape reconstruction particle swarm optimisation algorithm magnetic dipole model

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Declining multi inverter-based total harmonic distortion with the aid of hybrid optimisation technique

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2015年第3期9卷 285-293页

作者： Sathyanarayanan, Bharath Vaniyambadi Mani, Gopinath Bharath Univ Dept EEE Chennai Tamil Nadu India Dr NGP Inst Technol Dept EEE Coimbatore Tamil Nadu India

Multilevel inverter has arrived on the scene as an attractive and unique tool which is extensively employed for adapting dc (direct current) voltage to ac (alternating current) voltage. These appliances have recently taken shape as reliable sources of elevated power and found themselves well-utilised more often than not in industrial applications, by employing sine or adapted sine waves. The sine wave form generated in the multilevel inverter output has huge harmonics which should be minimised by appropriate steps. With a view to minimise the harmonics at the output signal, it is necessary to cut short the related total harmonic deformation (THD) of switching angles. Genetic algorithm is widely employed for the function of harmonic optimisation. However a hurdle appears as the cost function tends to invite further intricacy for the optimisation, entailing an added number of iterations. Therefore, with an eye on scaling down the number of iterations, the intention of genetic and particle swarm optimisation algorithms is to optimise the switching angles to the utmost. This method also leads to the decline of the related THD values. The innovative method is executed in the MATLAB/Simulink working platform.

关键词： harmonic distortion particle swarm optimisation genetic algorithms invertors waveform generators power semiconductor switches switching convertors iterative methods multilevel inverter-based total harmonic distortion declination hybrid optimisation technique elevated power sine waves IGBT switches switching function solitary converter semiconductor power converter harmonics minimisation total harmonic deformation genetic algorithm harmonic optimisation iteration method particle swarm optimisation algorithm switching angle optimisation THD Matlab Simulink

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