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Assessment of Renewable Energy Sources in Morocco using Economical Feasibility Technique

INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2019年第4期9卷 1856-1864页

作者： Kharrich, Mohammed Mohammed, Omar Hazem Akherraz, Mohammed Mohammed V Univ Mohammadia Sch Engineers Ibn Sina St PB 765 Rabat Morocco Northern Tech Univ Tech Coll Mosul Mosul 41002 Iraq

With the growing demand for harnessing renewable energy in the world instead of traditional energies in electricity generation, a diverse assessment of the performance of these energies has become necessary to make the most of them everywhere in the world. This paper addresses the economic assessment of renewable energy resources in different areas of Morocco. The project was implemented to obtain the optimum size and economical cost of the hybrid system at all sites under study in order to assess the available energy sources and find the best components of the system in each region. The Cost, reliability, ecology, and availability are the criteria respected in this study taken into consideration the same power load for all cases. The pso algorithm was chosen in the proposed research for its effectiveness and simplicity. The results show that the tidal energy is not approved in almost all regions of Morocco for its low speed. The PV/wind is the best configuration that gives a cost-effectiveness system mainly in Dakhla, which comprises the NPC $204467, COE $0.0842/kWh, LPSP 0.05%, Renewable fraction 100% and Availability of 95%. Furthermore, PV panels solar is the best component for production the energy in Morocco because of the high solar irradiation in this region of the world.

关键词： Loss of power supply probability Microgrid system Hybrid system Optimal economic Net present cost pso algorithm economic assessment

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Anti-windup scheme based on 2DOF-PI^λD^μ controller for velocity tracking of linear induction motor

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2019年第12期29卷

作者： Saleh, Ameer L. Obed, Adel A. Al-Yasir, Yasir I. A. Elfergani, Issa T. E. Rodriguez, Jonathan Clarke, Roger W. Abd-Alhameed, Raed A. Univ Misan Coll Engn Dept Elect Engn Misan Iraq Middle Tech Univ Elect Engn Tech Coll Baghdad Iraq Univ Bradford Sch Elect Engn & Comp Sci Bradford BD7 1DP W Yorkshire England Inst Telecomunicacoes Mobile Syst Grp Aveiro Portugal

Recently, a new scheme of conventional PID (Proportional Integral Derivative) controller has been utilized in different electrical drives for speed control. This paper presents an optimal P (ID mu)-D-lambda controller with 2DOF and anti-windup techniques based on pso optimization to produce the proposed controller which is exploited in order to control the velocity control of LIM. A three-phase inverter with SVPWM technique that is depended on approach of IFOC method is exploited to obtain the necessary output voltage supplied to the main windings of the LIM in order to control the velocity with minimum harmonics and good characteristics. The end effect is taken into account in dq axis equivalent circuit. Computed investigations show that the proposed controller is efficient in improving motor performance.

关键词： 2DOF-P I(lambda)D(mu)anti-windup IFOC linear induction motor modeling and simulation pso algorithm SVPWM velocity control

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A novel ensemble classification model based on neural networks and a classifier optimisation technique for imbalanced credit risk evaluation

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2019年 526卷 121073-000页

作者： Shen, Feng Zhao, Xingchao Li, Zhiyong Li, Ke Meng, Zhiyi Southwestern Univ Finance & Econ Sch Finance Chengdu 611130 Sichuan Peoples R China Southwestern Univ Finance & Econ Sch Stat Chengdu 611130 Sichuan Peoples R China Sichuan Univ Sch Business Chengdu 611130 Sichuan Peoples R China

Significant research has been performed on credit risk evaluation, with many machine learning and data mining techniques being employed for financial decision-making. The back propagation (BP) neural network has been a popular choice for credit risk evaluation problems, but many studies have found classifier ensembles to be superior to single classifiers. In this paper, a novel ensemble model based on the synthetic minority over-sampling technique (SMOTE) and a classifier optimisation technique is proposed for personal credit risk evaluation. To mitigate the negative effects of imbalanced datasets on the performance of the credit evaluation model, the SMOTE technique is used to rebalance the target training dataset. The particle swarm optimisation (pso) algorithm is employed to search for the best-connected weights and deviations in the BP neural networks. Based on the optimised BP neural network classifiers, an ensemble model is developed that combines the AdaBoost approach with the base classifiers. To ensure that the proposed model provides accurate and stable performance, we thoroughly explore and discuss the optimal parameters for the ensemble classification model. Finally, the proposed ensemble model is tested on German and Australian real-world imbalanced datasets. The results demonstrate that this model is more effective at processing credit data problems compared to the other classification models examined in this study. (C) 2019 Elsevier B.V. All rights reserved.

关键词： BP neural network Credit risk evaluation Ensemble classification model pso algorithm SMOTE

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An Adaptive Particle Swarm Optimization algorithm Based on Aggregation Degree

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2018年第4期11卷 443-448页

作者： Zhang, Xiuli Zhang, Ruihua Wang, Jianping Wang, Laidi Zhejiang Wanli Univ Coll Elect & Informat Engn Ningbo 315100 Zhejiang Peoples R China Ningbo Acad Oceanol & Fishery Ningbo 315010 Zhejiang Peoples R China

Objective: In view of the issue that particle swarm optimization algorithm is easy to fall into local optimal solution and low convergence precision when searching for the feasible space solution of complex multimodal functions, the paper proposes an adaptive particle swarm optimization algorithm (pso) based on aggregation degree. Methods: The algorithm firstly makes a random disturbance to search global optimal solution through differential evolution method according to the size of population aggregation to increase the diversity of particle swarm and improve its global searching ability. Then the adaptive adjustment is made to the inertia weight parameter of particles in accordance with the aggregation degree of particles. Conclusion: The simulation example of typical optimization problems suggests that this algorithm can improve the population diversity, particle activity, convergence performance and searching ability of pso.

关键词： Particle swarm optimization aggregation degree differential evolution inertia weight adaptive adjustment pso algorithm

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A Hybrid Cable Connection Structure for Wind Farms with Reliability Consideration

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IEEE ACCESS 2019年 7卷 144398-144407页

作者： Li, Junxian Hu, Weihao Wu, Xiawei Huang, Qi Liu, Zhou Chen, Zhe Blaabjerg, Frede Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 610054 Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

The collector system in wind farm has a large number of cables. When one of the cable fails, the power generated by the wind turbine (WT) cannot be collected into the substation through the faulty cable. That would make the profits for the wind farm reduced. Therefore, it is necessary to find a more reliable cable structure, which can transfer power as much as possible even if the cable failure occurs. In this paper, a new cable connection method is proposed in two main steps to improve both the reliability of the cable connection and the economic. Two different wind farms with the same climatological information and high voltage substation location are investigated and compared in the case study. In the first step, the minimum spanning tree (MST) algorithm is adopted to connect all wind turbines (WTs) to the substation. The cables used in collector system are the 33-kV middle voltage alternating current (MVAC) cables. Then the power production generated by WTs is transmitted from substation to the high voltage substation via a 132-kV transmission cable. The initial cable connection layout is obtained in the first step and the total trenching length is optimized to be minimum. In addition, cable selection in each branch can be determined based on the cable current carrying capacity. In the second step, reliability assessment is implemented by analyzing the expected energy not supplied (EENS). Based on EENS, the evaluation index LPCrel is obtained. This index takes both reliability and economy into account. Additional cables found by particle swarm optimization (pso) algorithm are added to the initial cable connection layout. Finally, a cable layout called hybrid structure is formed. What is more, by adding additional cables, the LPCrel is reduced by 1.5%. The simulation results clearly indicate that the proposed method is better when the cable failure is considered.

关键词： Wind farm MST algorithm reliability and economy pso algorithm cable connection layout

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Optimization of single and multi-areas economic dispatch problems based on evolutionary particle swarm optimization algorithm

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ENERGY 2018年 161卷 710-724页

作者： Mohammadian, M. Lorestani, A. Ardehali, M. M. Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Energy Syst LabCtr Excellence Power Syst Tehran Iran

Economic dispatch (ED) is a non-convex, non-linear, and non-smooth optimization problem that determines the optimal output power of generation units to meet the forecasted demand from an economic point of view. The objective of this study is to develop and examine the applicability of a newly developed evolutionary particle swarm optimization (E-pso) algorithm for optimization of the ED problem, where practical constraints, namely, valve-point effects, prohibited operating zones, multiple fuel usage, dynamic ramp rate limits, transmission losses, tie-line capacity, and spinning reserve are considered. In the developed E-pso algorithm, three operators including mutation, crossover, and selection are applied to enable the search process to skip local optimal points and enhance computational efficiency. To further enhance the performance of the algorithm, an approach is proposed to dynamically adjust the inertia, cognitive, and social weight coefficients to improve exploration and exploitation for smooth convergence. Upon validation of the E-pso algorithm by means of standard benchmark functions, four case studies including isolated and interconnected power systems are examined and the results are compared with those from other algorithms. The findings show that the proposed features enable the E-pso algorithm to successfully optimize the ED problem in lower simulation time, while all constraints are met. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Economic dispatch Optimization pso algorithm Power generation Operation constraints

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SURFACE-TO-AIR MISSILE PATH PLANNING USING GENETIC AND pso algorithmS

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JOURNAL OF THEORETICAL AND APPLIED MECHANICS 2017年第3期55卷 801-812页

作者： Zandavi, Seid Miad Payam Noor Univ Dept Engn Tehran Iran

Optimization algorithms use various mathematical and logical methods to find optimal points. Given the complexity of models and design levels, this paper proposes a heuristic optimization model for surface-to-air missile path planning in order to achieve the maximum range and optimal height based on 3DOF simulation. The proposed optimization model involves design variables based on the pitch programming and initial pitch angle (boost angle). In this optimization model, we used genetic and particle swarm optimization (pso) algorithms. Simulation results indicated that the genetic algorithm was closer to reality but took longer computation time. pso algorithm offered acceptable results and shorter computation time, so it was found to be more efficient in the surface-to-air missile path planning.

关键词： path planning genetic algorithm pso algorithm surface-to-air missile 3DOF simulation

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A Quantum Particle Swarm Optimization algorithm Based on Self-Updating Mechanism

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2018年第1期9卷 1-19页

作者： Wu, Shuyue Hunan Int Econ Univ Sch Informat Sci & Engn Field Elect Commun Sci Res Changsha Hunan Peoples R China Hunan Int Econ Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China

The living mechanism has limited life in nature;it will age and die with time. This article describes that during the progressive process, the aging mechanism is very important to keep a swarm diverse. In the quantum behavior particle swarm (Qpso) algorithm, the particles are aged and the algorithm is prematurely convergent, the self-renewal mechanism of life is introduced into Qpso algorithm, and a leading particle and challengers are introduced. When the population particles are aged and the leading power of leading particle is exhausted, a challenger particle becomes the new leader particle through the competition update mechanism, group evolution is completed and the group diversity is maintained, and the global convergence of the algorithm is proven. Next in the article, twelve Clement2009 benchmark functions are used in the experimental test, both the comparison and analysis of results of the proposed method and classical improved Qpso algorithms are given, and the simulation results show strong global finding ability of the proposed algorithm. Especially in the seven multi-model test functions, the comprehensive performance is optimal.

关键词： Aging Diversity Global Convergence pso algorithm Quantum Behaved Self-Renewal Mechanism

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A Heuristic Elastic Particle Swarm Optimization algorithm for Robot Path Planning

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INFORMATION 2019年第3期10卷 99页

作者： Wang, Haiyan Zhou, Zhiyu Zhejiang Police Vocat Acad Dept Secur Hangzhou 310018 Zhejiang Peoples R China Zhejiang Sci Tech Univ Dept Comp Hangzhou 310018 Zhejiang Peoples R China

Path planning, as the core of navigation control for mobile robots, has become the focus of research in the field of mobile robots. Various path planning algorithms have been recently proposed. In this paper, in view of the advantages and disadvantages of different path planning algorithms, a heuristic elastic particle swarm algorithm is proposed. Using the path planned by the A* algorithm in a large-scale grid for global guidance, the elastic particle swarm optimization algorithm uses a shrinking operation to determine the globally optimal path formed by locally optimal nodes so that the particles can converge to it rapidly. Furthermore, in the iterative process, the diversity of the particles is ensured by a rebound operation. Computer simulation and real experimental results show that the proposed algorithm not only overcomes the shortcomings of the A* algorithm, which cannot yield the shortest path, but also avoids the problem of failure to converge to the globally optimal path, owing to a lack of heuristic information. Additionally, the proposed algorithm maintains the simplicity and high efficiency of both the algorithms.

关键词： path planning pso algorithm A* algorithm elastic strategy

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Geometric optimization of radiative enclosures using pso algorithm

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2012年 60卷 61-69页

作者： Farahmand, A. Payan, S. Sarvari, S. M. Hosseini Shahid Bahonar Univ Kerman Dept Mech Engn Kerman 76175133 Iran Univ Sistan & Baluchestan Dept Mech Engn Zahedan 98135161 Iran

A Shape optimization of a two-dimensional radiative enclosure with diffuse-gray surfaces is studied by the particle swarm algorithm. The goal of the design problem is to achieve an optimized geometry to satisfy the desired temperature and heat flux distributions over a part of radiative enclosures, namely the design surface. The radiative transfer equation is solved by the discrete transfer method. In order to optimize the geometry of enclosure, the positions of the control points of the B-spline curves which represent the geometry of the enclosure are optimized. Some numerical examples are presented to demonstrate the effects of mesh refinements and shape parameters on the resultant geometry and the convergence rate. (C) 2012 Elsevier Masson SAS. All rights reserved.

关键词： Geometric optimization Inverse radiation design pso algorithm

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