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Study on offshore wind farm layout optimization based on decommissioning strategy 1

Study on offshore wind farm layout optimization based on dec...

Joint Conference of the World Engineers Summit / Applied Energy Symposium and Forum - Low Carbon Cities and Urban Energy (WES-CUE)

作者： Sun, Haiying Yang, Hongxing Gao, Xiaoxia Hong Kong Polytech Univ Dept Bldg Serv Engn Renewable Energy Res Grp Hong Kong Hong Kong Peoples R China North China Elect Power Univ Baoding Dept Power Engn Baoding Peoples R China

In recent years, along with the first generation of wind power reaching retirement age, the high decommissioning cost arises with more and more attention all around the word. To reduce this huge cost, an innovative offshore wind farm layout optimization method based on decommissioning strategy is presented in this paper. In the optimization method, the decommissioning strategy means that the foundations can be reused after the retirement of the first generation of wind turbines, and then smaller second generation wind turbines will be installed on the original foundations. The optimization process is based on the multi-population genetic algorithm (MPGA). A conceptual two-dimensional (2D) wake model is adopted to calculate wind losses caused by wake effect. The Cost of Energy (COE) is regarded as the criteria to judge the effectiveness of this new method. The way to estimate costs will also be introduced in this study. Finally, a case study in Waglan sea area in Hong Kong is analyzed and discussed. From the case results, Hong Kong is an ideal region to develop the offshore wind industry, and the proposed optimization method can reduce the COE down to 1.02 HK$/kWh. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： Offshore wind farm layout optimization Decommissioning strategy multi-population genetic algorithm Cost of energy

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Design on Optimization of Phase for Urban Traffic Coordinated Control 10

Design on Optimization of Phase for Urban Traffic Coordinate...

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10th International Symposium on Computational Intelligence and Design (ISCID)

作者： Wang, Hao Huang, Feng Wang, Hongcai Chen, Dong Shanghai Inst Technol Sch Comp Sci & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9781538636756

The optimization of signal cycle and green ratio for intersection of coordinated control can improve the traffic performance, but it is difficult to obtain the best two-way bandwidth in the smaller space of solution which can be approximated optimization, in order to reduce more the vehicle delay at intersection, the space of optimal solution is expanded by adjusting phase, based on multi-population genetic algorithm, the scheme of phase optimized design was proposed. According to the principle of vehicles reach steadily, the vehicle delay model was established. The minimum of vehicle average delay was regarded as the evaluation index for coordinated control of benefit and the multi-population genetic algorithm was used to implement the automatic optimization of the phase, which is simulated and verified by VISSIM. The result shows that the method of phase optimized design can reduce the average vehicles delay by 5% and reduce the average number of parking by 28.9%, which can effectively improve the rapidity and effectiveness of the method of combination and traversal.

关键词： traffic engineering coordinated control phase optimization multi-population genetic algorithm

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An evolutionary multi population approach for test data generation

An evolutionary multi population approach for test data gene...

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World Congress on Nature and Biologically Inspired Computing

作者： Deepak, Anupama Samuel, Philip Cochin Univ Sci & Technol Dept Comp Sci Cochin 682022 Kerala India Cochin Univ Sci & Technol Informat Technol Cochin 682022 Kerala India

ISBN: (纸本)9781424450534

In this paper we propose an approach for test data generation using genetic algorithm. Our objective is to design a multi-population genetic algorithm using uniform crossover. In this paper we analyze the performance of proposed uniform crossover multi population genetic algorithm method with different combinations of factors that influence the test data generation strategy. For implementing multi-population genetic algorithm, random migration is used and individuals are added to the existing subpopulation. Here we have also compared the single population approach and multi-population approach to determine which of these are effective towards generation of test data. By combining the individuals in the subpopulation using uniform cross over the test data generated will have better chance of existence.

关键词： Test data generation genetic algorithm multi-population genetic algorithm Fitness function Uniform crossover

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Wind Farm Layout Optimization Considering Commercial Turbine Selection and Hub Height Variation

Wind Farm Layout Optimization Considering Commercial Turbine...

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作者： Mamdouh Ahmad Abdulrahman University Of Calgary

学位级别：博士

New aspects were added to the wind farm layout optimization problem; commercial turbine selection, generic realistic representation for the thrust coefficient, investigating the power-cost of energy trade-off range, and introducing the wind farm layout upgrade optimization problem. A range of commercial turbines was selected and the manufacturers' power curves were used to evaluate the power developed by each turbine using the effective wind speed. The classical Jensen's wake model was implemented to simulate the wake and wake interference within the farm in an analytic and accurate way. A simple field-based cost model was developed to evaluate the cost of any layout in terms of the turbine rated power and hub height. For the upgrade cases, the cost model included an area factor to account the upgraded area to the original farm area. A genetic algorithm was used for optimization throughout this dissertation. A technique called Random Independent multi-population genetic algorithm was used in some cases to accelerate the optimization. The results showed that co-operative optimization is superior over the selfish one. The turbine aerodynamic efficiency was found to magnify the difference between the two optimization strategies. A wide range of commercial turbines was selected and a useful range of power-cost of energy trade-off was obtained. The optimization was found to be more efficient in offshore cases because of the low entrainment coefficient in the wake model. The Random Independent multi- population technique caused a significant reduction in the speed of the optimization. It is likely that most farms can be efficiently and practically upgraded with a wide range of power-cost of energy trade-offs, using the proposed upgrade layout and the optimization objective.

关键词： Wind farm layout optimization Layout upgrade optimization Commercial turbine selection Hub height variation multi-population genetic algorithm Thrust coefficient Aerodynamic efficiency Co-operative optimization Selfish optimization

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Prediction of resilient modulus for subgrade soils based on ANN approach

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Journal of Central South University 2021年第3期28卷 898-910页

作者： ZHANG Jun-hui HU Jian-kun PENG Jun-hui FAN Hai-shan ZHOU Chao National Engineering Laboratory of Highway Maintenance Technology Changsha University of Science&TechnologyChangsha 410114China Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China

The resilient modulus(MR)of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement *** order to determine the resilient modulus of compacted subgrade soils quickly and accurately,an optimized artificial neural network(ANN)approach based on the multi-population genetic algorithm(MPGA)was proposed in this *** MPGA overcomes the problems of the traditional ANN such as low efficiency,local optimum and *** developed optimized ANN method consists of ten input variables,twenty-one hidden neurons,and one output *** physical properties(liquid limit,plastic limit,plasticity index,0.075 mm passing percentage,maximum dry density,optimum moisture content),state variables(degree of compaction,moisture content)and stress variables(confining pressure,deviatoric stress)of subgrade soils were selected as input *** MR was directly used as the output ***,adopting a large amount of experimental data from existing literature,the developed optimized ANN method was compared with the existing representative estimation *** results show that the developed optimized ANN method has the advantages of fast speed,strong generalization ability and good accuracy in MR estimation.

关键词： resilient modulus subgrade soils artificial neural network multi-population genetic algorithm prediction method

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A Strategy for multi-target Paths Coverage by Improving Individual Information Sharing

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2019年第11期13卷 5464-5488页

作者： Qian, Zhongsheng Hong, Dafei Zhao, Chang Zhu, Jie Zhu, Zhanggeng Jiangxi Univ Finance & Econ Sch Informat Management Nanchang 330013 Jiangxi Peoples R China

The multi-population genetic algorithm in multi-target paths coverage has become a top choice for many test engineers. Also, information sharing strategy can improve the efficiency of multi-population genetic algorithm to generate multi-target test data;however, there is still space for some improvements in several aspects, which will affect the effectiveness of covering the target path set. Therefore, a multi-target paths coverage strategy is proposed by improving multi-population genetic algorithm based on individual information sharing among populations. It primarily contains three aspects. Firstly, the behavior of the sub-population covering corresponding target path is improved, so that it can continue to try to cover other sub-paths after covering the current target path, so as to take full advantage of population resources;Secondly, the populations initialized are prioritized according to the matching process, so that those sub-populations with better path coverage rate are executed firstly. Thirdly, for difficultly-covered paths, the individual chromosome features which can cover the difficultly-covered paths are extracted by utilizing the data generated, so as to screen those individuals who can cover the difficultly-covered paths. In the experiments, several benchmark programs were employed to verify the accuracy of the method from different aspects and also compare with similar methods. The experimental results show that it takes less time to cover target paths by our approach than the similar ones, and achieves more efficient test case generation process. Finally, a plug-in prototype is given to implement the approach proposed.

关键词： multi-population genetic algorithm individual information sharing multi-target paths coverage contact layer proximity test case

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multi-Task Optimization-Based Test Data Generation for Mutation Testing via Relevance of Mutant Branch and Input Variable

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IEEE ACCESS 2020年 8卷 144401-144412页

作者： Dang, Xiangying Yao, Xiangjuan Gong, Dunwei Tian, Tian Sun, Baicai China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Xuzhou Univ Technol Sch Informat Engn Sch Big Data Xuzhou 221000 Jiangsu Peoples R China Key Lab Intelligent Ind Control Technol Jiangsu P Xuzhou 221000 Jiangsu Peoples R China China Univ Min & Technol Sch Math Xuzhou 221116 Jiangsu Peoples R China Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250101 Peoples R China

Mutation testing is a powerful software testing technique. However, it is difficult to obtain test data for killing a large number of mutants, especially for hard-to-kill mutants. Mutant branch is formed by an original statement of a program under test and its mutated statement. The true branch of a mutant branch is covered by a test datum, suggesting the corresponding mutant is killed under the criterion of weak mutation testing. This article focuses on efficiently generating test data for a large number of mutant branches. When generating test data using a search-based method, the size of the search domain is a determining factor affecting the search performance. The key observation is that only partial input variables affect whether a mutant will be killed, so the search domain can be reduced by deleting irrelevant variables. Along this line, we first group the mutant branches based on their relevant input variables, followed by formulating a multi-task optimization model of test data generation for the grouped mutant branches, in which the relevant input variables are taken as the decision variables. Finally, a multi-population genetic algorithm with individual sharing is employed to generate test data by multi-tasking. The experiments based on eight programs of various sizes show that removing related variable helps reduce the search domain, and the efficiency of test data generation by grouping and multitasking is improved.

关键词： Input variables Optimization genetic algorithms Data models Task analysis Software testing Weak mutation testing multi-task test data generation relevant input variables grouping mutants multi-population genetic algorithm

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Few shot intelligent assessment method for compaction quality of earthwork considering uncertainty

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 256卷

作者： Deng, Naifu An, Zaizhan Hao, Jiawang Zhang, Qinglong Univ Sci & Technol Beijing Sch Civil & Resource Engn 30 Xueyuan Rd Beijing 100083 Peoples R China China Renewable Energy Engn Inst 2 Liupu Kang North St Beijing 100120 Peoples R China Shijiazhuang Tiedao Univ Coll Safety Engn & Emergency Management 17 North 2nd Ring Rd East Shijiazhuang 050043 Hebei Peoples R China

Current practices lack reliable models for continuously assessing compaction quality from limited data, which hinders effective compaction quality control and the acceptance of earthwork. Using material source parameters as inputs for assessment models makes it infeasible to achieve a real-time assessment of the compaction quality in earthwork. These issues have hindered the popularization of emerging intelligent compaction (IC) and intelligent rolling compaction (IRC) technologies. Therefore, this study addresses the need for small-sample training by introducing an uncertainty-based multi-population genetic algorithm fused with a backpropagation neural network model (UB-MPGA-BP), enabling the real-time intelligent assessment of compaction quality based on few-shot learning. In case studies involving high- and low-liquid limit silts, existing models trained on large datasets were notably inadequate for small-sample evaluation tasks. However, the proposed method not only performed well in silt cases but also exhibited high performance in rockfill materials. The analysis revealed that in small-sample scenarios without reliance on material source parameters, the proposed method has the advantages of real-time accuracy, generalization, few-shot data-driven, and simple input parameters, which enable the rapid assessment of compaction quality. The model can potentially facilitate IC/IRC applications in other engineering practices.

关键词： Real-time intelligent assessment Compaction quality control and acceptance Few-shot learning Uncertainty analysis multi-population genetic algorithm

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Effects of stage number of condensing process on the power generation systems for LNG cold energy recovery

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Applied Thermal Engineering 2017年 126卷 566-582页

作者： Junjiang Bao Yan Lin Ruixiang Zhang Ning Zhang Gaohong He State Key Laboratory of Fine Chemicals School of Petroleum and Chemical Engineering Dalian University of Technology Panjin 124221 China

Due to the large temperature change of LNG regasification process (from about −160 °C to ambient temperature), the irreversible loss of the LNG regasification process is large, which affects the efficiency of power generation systems for LNG cold energy recovery. Our previous study proposed a two-stage condensation Rankine cycle, which can effectively improve the utilization efficiency of LNG. On this basis, this paper continues to discuss the effect of stage number of condensing process on the performance and economy of the power generation systems by LNG cold energy. Seven systems are considered in this paper, including three traditional power generation systems (direct expansion, organic rankine cycle and combined cycle) and four proposed systems (the two-stage and three-stage condensation Rankine cycles, as well as the two-stage and three-stage combined cycles). These seven systems are simulated by Aspen Hysys software, and with net power output, electricity production cost (EPC) and annual net income as the objective functions respectively, the key system parameters (the condensation temperature and the inlet pressure of LNG turbine, etc.) are optimized under the three different objective functions. Results showed that when the net power output is the objective function, the net power output of the three-stage combined cycle is the largest. If EPC is chosen as the objective function, the cost of the combined cycle is always lower than that of the Rankine cycle system with the same condensation stage number, but net power output of the Rankine cycle is higher than that of the combined cycle. When annual net income is the objective function, the three-stage combined cycle has the largest annual net income. But if considering the difficult degree of control system, the two-stage combined cycle is recommended.

关键词： multi-stage condensation Rankine cycle Liquefied natural gas Power generation multi-population genetic algorithm

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Study on offshore wind farm layout optimization based on decommissioning strategy

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Energy Procedia 2017年 143卷 566-571页

作者： Haiying Sun Hongxing Yang Xiaoxia Gao Renewable Energy Research Group Department of Building Services Engineering The Hong Kong Polytechnic University Hong Kong Department of Power Engineering North China Electric Power University (Baoding) Baoding PR China

In recent years, along with the first generation of wind power reaching retirement age, the high decommissioning cost arises with more and more attention all around the word. To reduce this huge cost, an innovative offshore wind farm layout optimization method based on decommissioning strategy is presented in this paper. In the optimization method, the decommissioning strategy means that the foundations can be reused after the retirement of the first generation of wind turbines, and then smaller second-generation wind turbines will be installed on the original foundations. The optimization process is based on the multi-population genetic algorithm (MPGA). A conceptual two-dimensional (2D) wake model is adopted to calculate wind losses caused by wake effect. The Cost of Energy (COE) is regarded as the criteria to judge the effectiveness of this new method. The way to estimate costs will also be introduced in this study. Finally, a case study in Waglan sea area in Hong Kong is analyzed and discussed. From the case results, Hong Kong is an ideal region to develop the offshore wind industry, and the proposed optimization method can reduce the COE down to 1.02 HK$/kWh.

关键词： Offshore wind farm layout optimization Decommissioning strategy multi-population genetic algorithm Cost of energy

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