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Active power optimisation for wind farms under generator inter-turn short-circuit fault

IET RENEWABLE POWER GENERATION 2020年第12期14卷 2079-2088页

作者： Ma, Kuichao Soltani, Mohsen Hajizadeh, Amin Zhu, Jiangsheng Chen, Zhe Aalborg Univ Dept Energy Technol Niels Bohrs Vej 8 Esbjerg Denmark SEWPG European Innovat Ctr Inge Lehmanns Gade 10 Aarhus Denmark Aalborg Univ Dept Energy Technol Pontoppidanstr 111 Aalborg Denmark

Inter-turn short-circuit fault of the stator winding is one of the most common faults of asynchronous generators and often found in doubly-fed wind turbines. The improper treatment methods will lead to unnecessary power loss or further damage to the insulation layer. To solve this problem, an optimal power dispatch strategy (OPDS) is proposed which combines the control at the turbine level and the optimisation at the farm level. At the turbine level, the faulty turbine is down-regulated based on the principle that the fault current does not exceed the rated current. At the farm level, particle swarm optimisation is used to optimise the power references considering wake effects to compensate for the power loss caused by the downregulated turbine. The effectiveness of OPDS is illustrated by comparing with two usually used strategies, one takes proportional dispatch strategy (PDS) and directly shuts down the faulty turbine, the other takes PDS but ignores the fault. The results show that OPDS can minimise power loss under the premise of protecting the faulty turbine. The study contributes to reduce the maintenance costs of wind farms and improve the operational capability of the wind turbine under fault conditions.

关键词： power engineering computing power generation dispatch wind power plants optimisation wind turbines power generation control fault diagnosis stators particle swarm optimisation asynchronous generators turbine level faulty turbine farm level particle swarm optimisation algorithm power references down-regulated turbine OPDS proportional dispatch strategy wind farms wind turbine fault conditions active power optimisation generator inter-turn short-circuit fault stator winding asynchronous generators doubly-fed wind turbines improper treatment methods unnecessary power loss insulation layer optimal power dispatch strategy

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Mitigating the effects of geomagnetically induced currents in the power network

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第23期14卷 5514-5525页

作者： Hesami Naghshbandy, Ali Ghaderi Baayeh, Arman Faraji, Ayda Univ Kurdistan Fac Engn Dept Elect Engn Sanandaj Kurdistan Iran

Geomagnetically induced currents (GICs) in long high-voltage transmission lines cause half-cycle saturation of transformers, malfunction of protective relays, reactive power loss, and voltage instability. This study describes the design for a GIC effects mitigation device (GEMD) that can be installed at a power system autotransformer common node connection to block the flow of GIC DC flux at the cores of the autotransformers. The devices consist of variable resistors, which are controlled by a wide-area method based on particle swarm optimisation algorithm. The effectiveness of the proposed protective electrical devices is investigated and validated through simulation studies under many operational cases. The test results obtained from 648 cases verify the high performance of GEMD. The proposed method can mitigate the reactive power loss due to GIC in the power system with average improvements by 7.9 and 7.09% over resistive and capacitive blocking devices, respectively.

关键词： power grids autotransformers transformer windings relay protection power transmission lines geomagnetism reactive power particle swarm optimisation power transmission protection reactive power loss resistive blocking devices capacitive blocking devices geomagnetically induced currents power network high-voltage transmission lines protective relays voltage instability mitigation device GEMD power system autotransformer common node connection GIC DC flux autotransformers wide-area method particle swarm optimisation algorithm protective electrical devices

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Shape and electric performance improvement of an insulator string using particles swarm algorithm

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2020年第2期14卷 198-205页

作者： Doufene, Dyhia Bouazabia, Slimane Haddad, Abderrahmane Univ Sci & Technol Houari Boumediene Elect & Ind Syst Lab LSEI Bab Ezzouar Algeria Cardiff Univ Sch Engn Adv High Voltage Engn Res Ctr Cardiff CF24 3AA Wales

In this study, a particle swarm optimisation (PSO) algorithm coupled with finite-element method (FEM) is implemented to improve the performance of a cap-and-pin insulator by reducing the value of maximum electric field strength. Two goals are set in this work: (i) optimisation of the tangential electric field distribution and magnitude (which is taken as the objectif function to be minimised) to reduce the risk of flashover due to surface pollution, and (ii) reduction of the creepage distance (by adopting adequate constraints) leading to a reduction of the surface area and consequently a decrease in the insulator weight. The investigation is divided into two parts;first, one-unit insulator is optimised with which a chain is constructed, and its performance compared with the reference insulator string. The second part considers a whole chain of four-unit insulator whose performance is optimised and compared to that of a reference insulator string. The main finding of this work indicates that, if an insulator string is constructed using an optimised cap-and-pin unit, the performance of the so-formed string is also optimised. The optimisation of a complete string leads to practically the same performance as that of a string obtained by assembling optimised insulator units.

关键词： electric fields insulators finite element analysis flashover ceramic insulators particle swarm optimisation insulator contamination surface area insulator weight one-unit insulator four-unit insulator insulator string cap -pin unit complete string optimised insulator units electric performance improvement particles swarm algorithm particle swarm optimisation algorithm finite-element method -pin insulator maximum electric field strength tangential electric field distribution objectif function

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Structural evolution and ligand effects of (Au1L2)n, (Au2L3)n, and (Au3L4)n [n =1-3, L = SCH3,P(CH3)2,PH2,Cl] clusters

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MOLECULAR SIMULATION 2020年第1期46卷 1-8页

作者： Ma, Miaomiao Liu, Liren Zhu, Hengjiang Lu, Junzhe Tan, Guiping Xinjiang Normal Univ Coll Phys & Elect Engn Urumqi Peoples R China Key Lab Mineral Luminescence Mat & Micro Struct X Urumqi Peoples R China

In this paper, we present a density-functional theory with a particle swarm optimisation algorithm to research the progress of the ligand effects of the gold (Au) structures and growth process of the [Au1L2](n), [Au2L3](n) and [Au3L4](n) (n = 1-3, L = SCH3, PH2, P(CH3)(2),Cl) nanoclusters. Changing of ligand atoms induces minor changes in the Au and Au-L bond lengths. When L = Cl, with increasing n, the size of the Au cores tends to increase, while when L = PH2, with increasing n, the size of Au cores decreases and the length of the ligand protection increases. We give a detailed comparison of the average binding energy, stability, and aromaticity of these clusters and further explain the diverse role of ligand substitution. The order of stability for each ligand, Cl > PH2> SCH3>P(CH3)(2), is determined by the average binding energy and the difference between the atomic energies. In addition, the influence of strong Au-Au interactions will weaken the aromaticity of the structure. New nanoclusters that can be widely applied in other fields, and designed by leveraging the ligand effects of the Au structural evolution characteristics.

关键词： Density-functional theory particle swarm optimisation algorithm ligand effects structural evolution

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Robust scheme for voltage regulation and power sharing among DERs in DC microgrids

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IET RENEWABLE POWER GENERATION 2020年第4期14卷 647-657页

作者： Malek, Sajjad Gholipour, Mehdi Univ Isfahan Dept Elect Engn Esfahan Iran

This study presents a new control scheme to provide both voltage regulation and power sharing within a DC microgrid. It includes both primary and secondary levels as well as the proposed auxiliary feedback. The secondary control distributes and uses the consensus algorithm to share power. First, the auxiliary feedback is formulated. Then the features of the feedback are analytically extracted to provide both voltage regulation and power sharing. Next, the particle swarm optimisation algorithm determines the parameters of the controller optimally. Following that, a series of simulations is carried out to show the performance of the proposed scheme in comparison with the conventional methods. Hence, to validate the advantages of the proposed scheme, a four distributed energy resource (DER) DC microgrid is considered, and the proposed approach is applied. Afterwards, the eigenvalue and sensitivity analysis show the effect of the parameters variation on microgrid stability. The results show that the applied method can regulate the voltage well and shares the power in the presence of the load demand as a disturbance besides the long-time delay associated with the secondary control as an uncertainty.

关键词： eigenvalues and eigenfunctions distributed power generation sensitivity analysis feedback voltage control particle swarm optimisation robust control optimal control power system stability power system control secondary control voltage regulation particle swarm optimisation algorithm distributed energy resource DC microgrid microgrid stability robust scheme power sharing optimal control eigenvalue sensitivity analysis

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Performance analysis of cooperative PDMA with AF relaying over Rayleigh fading channels

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IET COMMUNICATIONS 2020年第13期14卷 2166-2175页

作者： Zhang, Xiurong Kang, Shaoli Fu, Xueqian Tang, Wanwei Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China China Acad Telecommun Technol State Key Lab Wireless Mobile Commun Beijing 100191 Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Tangshan Univ Intelligence & Informat Engn Coll Tangshan 063000 Peoples R China

This study investigates the performances of cooperative pattern division multiple access (PDMA) technology with a half-duplex amplify-and-forward (AF) relaying over Rayleigh fading channels. To characterise the performances of the AF-PDMA system, the closed-form expressions of the exact outage probabilities, system throughput and diversity order are derived, respectively. The particle swarm optimisation algorithm based on penalty function is used to obtain the optimum power allocation coefficients by maximising the system throughput. By setting different values on the target data rate for users to analyse the AF-PDMA system throughput performance, simulation results demonstrate the performance enhancement of the considered AF-PDMA compared with orthogonal multiple access with AF relaying and PDMA with decode-and-forward relaying.

关键词： amplify and forward communication Rayleigh channels fading channels probability particle swarm optimisation performance analysis Rayleigh fading channels pattern division multiple access half-duplex amplify-and-forward AF-PDMA system closed-form expressions exact outage probabilities system throughput particle swarm optimisation algorithm optimum power allocation coefficients performance enhancement considered AF-PDMA orthogonal multiple access

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Estimation of Bearing Capacity of Piles in Cohesionless Soil Using Optimised Machine Learning Approaches

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2020年第2期38卷 2271-2291页

作者： Kardani, Navid Zhou, Annan Nazem, Majidreza Shen, Shui-Long RMIT Univ Sch Engn Civil & Infrastruct Engn Discipline Melbourne Vic 3001 Australia Shantou Univ Coll Engn Dept Civil & Environm Engn Shantou 515063 Guangdong Peoples R China

Accurate estimation of the bearing capacity of piles requires complex modelling techniques which are not justified by timeframe, budget, or scope of the projects. In this study, six advanced machine learning algorithms including decision tree, k-nearest neighbour, multilayer perceptron artificial neural network, random forest, support vector regressor and extremely gradient boosting are employed to model the bearing capacity of piles in cohesionless soil, and the particle swarm optimisation algorithm is used to optimate the hyper-parameters of machine learning algorithms. A dataset comprising of 59 cases is employed and the R-squared value, root mean square error and variance accounted for are used as performance metrics to compare the performance of optimised machine learning methods. The comparison reveals that the optimised machine learning methods have great potential to estimate bearing capacity of piles and the particle swarm optimisation algorithm is efficient in the hyper-parameter tuning. The results show that R-squared values of six optimised machine learning approaches on the testing set vary from 0.731 to 0.9615. Also, the optimised extremely gradient boosting (R-squared value = 0.9615) shows the best performance compared with other algorithms. Furthermore, the relative importance of influential variable is investigated, which shows that effective stress is the most influential variable for bearing capacity of piles with an importance score of 30.9%. In addition, the results by the optimised machine learning method are compared to the beta-method which is a popular empirical method. It is revealed the prominent performance of optimised machine learning approaches.

关键词： Optimised machine learning algorithms particle swarm optimisation algorithm Bearing capacity of piles Relative variable importance

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Optimization of the Dynamic Performance of the Photo-Voltaic and Wind-Turbine Hybrid-Energy System by the particle-swarm Optimization Technique 21

Optimization of the Dynamic Performance of the Photo-Voltaic...

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21st International Middle East Power Systems Conference (MEPCON)

作者： Awad, Hilmy Fathy, Mostafa S. Hegazy, Hosam Elkholy, E. E. Helwan Univ Fac Ind Educ Cairo Egypt Minist Higher Educ Coll Technol Cairo Egypt Menoufia Univ Fac Engn Engn Dept Shibin Al Kawm Al Minufiyah Egypt

ISBN: (纸本)9781728152899

In order to overcome the intermittence of renewable energy sources, such sources are connected in parallel forming hybrid systems. One of these hybrid systems is presented in this paper aiming to investigate and improve its dynamic performance. The hybrid energy system is composed of photovoltaic (PV) arrays and wind turbine (WT) that drives a doubly-fed induction generator (DFIG). Both the PV and WT are linked together through a DC-capacitor link. In order to improve the dynamic performance, a particle-swarm Optimization algorithm is implemented to tune the gains of the applied controllers. The obtained optimum gains are then implemented in a simulation model using the simulink program (MATLAB). Results in the case of optimized gains are compared with initial-design results in the relevant literature. Results show that the dynamic performance of the hybrid energy system with PSO is improved in terms of speed and steady-state error.

关键词： Doubly-Fed Induction Generator (DFIG) wind turbine Maximum Power Point Tracking (MPPT) particle swarm optimisation algorithm Photovoltaic (PV)

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Techno-economic potential of battery energy storage systems in frequency response and balancing mechanism actions

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JOURNAL OF ENGINEERING-JOE 2020年第9期2020卷 774-782页

作者： Kirli, Desen Kiprakis, Aristides Univ Edinburgh Inst Energy Syst Sch Engn Faraday BldgKings BldgColin Maclaurin Rd Edinburgh EH9 3DW Midlothian Scotland

Batteries offer a combination of balancing and regulation services within a smart grid to improve its resilience and flexibility. Maintaining an acceptable state of health and the highest rate of return requires dynamic modelling of the asset and rigorous optimisation. The authors compare the technical cost and economic benefit of battery employment in dynamic frequency and balancing mechanism actions in a smart grid. They use the services procured by National Grid in the UK as a case study but the methodology is globally applicable, including developing grid infrastructures. Their methodology yields the most optimum scenario of service participation, accounting for the dynamic degradation and considering variable pricing of electricity throughout the day. Additionally, it advises the most optimal despatch schedule and price declarations for the battery over the course a day and a year, employing particle swarm optimisation algorithm and historic data. Their results demonstrate that ordinarily frequency response is preferred due to its lower technical toll and payments for availability rather than despatch. However, the proposed despatch schedule including both services provides the highest profit. They anticipate this methodology to become the basis for more sophisticated battery models that integrate the service despatch optimisation, dynamic lifetime degradation and economic analysis.

关键词： optimisation smart power grids profitability battery storage plants scheduling pricing energy storage particle swarm optimisation power generation economics secondary cells grid infrastructures optimum scenario service participation dynamic degradation optimal despatch schedule price declarations particle swarm optimisation algorithm ordinarily frequency response lower technical toll highest profit sophisticated battery models dynamic lifetime degradation economic analysis techno-economic potential battery energy storage systems balancing mechanism actions regulation services smart grid flexibility dynamic modelling rigorous optimisation technical cost economic benefit battery employment dynamic frequency National Grid

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Path planning of autonomous agricultural machineries in complex rural road

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JOURNAL OF ENGINEERING-JOE 2020年第6期2020卷 239-245页

作者： Cui, Jinbo Zhang, Xianfeng Fan, Xinmiao Feng, Wei Li, Ping Wu, Yu Chongqing Acad Agr Sci Chongqing Peoples R China Sichuan Normal Univ Coll Movie & Media Chengdu Peoples R China

This study mainly investigates the path planning and collision avoidance problem of autonomous agricultural machineries in the complex rural road. First, the grid model is used to build the global map of machineries and the particle swarm optimisation algorithm is used to search the global path. A non-linear dynamic inertia weight is introduced to avoid the local lock-up problem of traditional particle swarm optimisation. Then the artificial potential energy method for addressing the local collision problem is used, which is the sum of the attraction of the target point and the repulsion of the obstacles and the road boundary force. To solve the problem of the slip and roll of autonomous machinery caused by complex roads, a machinery dynamic model considering road curvature and topographic inclination is established, and the model predictive control algorithm is used to track the planned path. The simulation results show that proposed method can make the autonomous agricultural machinery driving safely and smoothly.

关键词： predictive control collision avoidance particle swarm optimisation agricultural machinery nonlinear dynamic inertia weight traditional particle swarm optimisation road boundary force autonomous machinery model predictive control algorithm autonomous agricultural machinery path planning collision avoidance problem particle swarm optimisation algorithm machinery dynamic model

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