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Voltage regulation methods for active distribution networks considering the reactive power optimization of substations

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APPLIED ENERGY 2021年 284卷 116347-116347页

作者： Ma, Wei Wang, Wei Chen, Zhe Wu, Xuezhi Hu, Ruonan Tang, Fen Zhang, Weige Beijing Jiaotong Univ Natl Act Distribut Network Technol Res Ctr Beijing Peoples R China Aalborg Univ Dept Energy Technol Aalborg Denmark

Due to the increasing penetration of photovoltaic (PV) power systems in active distribution networks (ADNs), PV power fluctuations may result in significant voltage variations of ADNs. Therefore, this paper proposes a voltage regulation method for ADNs to minimize the operational losses while keeping the nodal voltages within the limit with the reduced PV power curtailment and the reduced switching numbers of on-load tap changers (OLTCs) and capacitor banks (CBs). Meanwhile, the proposed voltage regulation method also aims to minimize the reactive power flowing through OLTCs, and to minimize the switching numbers of substation CBs. In this study, the centralized voltage regulation is performed based on the worst voltage variation scenarios of ADNs, where a multi-objective mixed integer nonlinear programming (MINP) model with time-varying decision variables is established. The MINP model is solved using the non-dominated sorting genetic algorithm ii (NSGA-ii), and a practical decision-making algorithm is developed to select the best solution from the Pareto optimal set. Moreover, the decentralized voltage regulation aims at mitigating real-time nodal voltage variations via adjusting the real-time active and reactive power of each PV plant. Several simulations and comparisons are carried out on a modified IEEE 33-node system to verify the effectiveness of the proposed methods, and to compare with some previous voltage regulation methods. Simulation results show that the proposed voltage regulation methods can not only effectively control voltage variations of ADNs but also improve the economics of ADNs, substations, and PV plants.

关键词： Active distribution network Voltage regulation Photovoltaic power fluctuations Voltage variations Multi-objective mixed integer nonlinear programming non-dominated sorting genetic algorithm ii

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A hybrid approach to solve a bi-objective optimization problem of a capacitated-flow network with a time factor

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2020年 204卷 107191-107191页

作者： Yeh, Cheng-Ta Fu Jen Catholic Univ Dept Informat Management New Taipei Taiwan

The problem of system reliability evaluation for a capacitated-flow network has been extended by involving the characteristics of the quickest path problem. System reliability is thus defined as the probability that d units of demand can be successfully delivered from a source node to a sink node within time t. System reliability optimization considering the time factor is important for demand transmission, especially for communication systems. However, this factor has not been taken into account in previous research. To this end, in this study, the time factor is employed to adopt a component assignment strategy to maximize the system reliability and minimize the assignment cost. Because the considered problem is bi-objective, a hybrid approach integrating the non-dominated sorting genetic algorithm ii, a concept of minimal path, information entropy, and simple additive weighting is developed to determine a set of non-dominated solutions and to subsequently determine the compromise solution from an objective viewpoint. Numerical examples are utilized to demonstrate the computational efficiency of the proposed method.

关键词： Capacitated-flow network Quickest path problem Time factor System reliability Component assignment non-dominated sorting genetic algorithm ii Minimal path Information entropy Simple additive weighting

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Multi-objective optimization of Binary Flashing Cycle (BFC) driven by geothermal energy

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APPLIED THERMAL ENGINEERING 2020年第0期166卷 114693-000页

作者： Wang, Lingbao Li, Huashan Bu, Xianbiao Chinese Acad Sci Guangzhou Inst Energy Convers Guangzhou 510640 Peoples R China CAS Key Lab Renewable Energy Guangzhou 510640 Peoples R China Guangdong Prov Key Lab New & Renewable Energy Res Guangzhou 510640 Peoples R China

The binary flashing cycle (BFC) has been demonstrated to be a promising technology for geothermal energy utilization due to higher net power output. Considering both thermodynamic and economic factor simultaneously, the multi-objective optimization on the BFC has been investigated using non-dominated sorting algorithm-ii. Two conflicting objectives including net power output and heat exchanger area of the system are taken into account to build a set of Pareto optimal solutions. The results show that the system achieve the maximum net power output with maximum heat exchanger area, and the minimum net power output with the minimum heat exchanger area. The relationships between the net power output and heat exchanger area in the Pareto frontier solution are fitted. TOPSIS method (Technique for Order Preference by Similarity to Ideal Situation) is applied to select the final solution on the Pareto frontier. The effects of weight factor for net power output on the objectives and decision variables have also been investigated, which are good references to the decision makers. The recommended weight factor for net power output is in the range of 0.1-0.6.

关键词： Geothermal energy Binary flashing cycle Multi-objective optimization non-dominated sorting genetic algorithm ii TOPSIS method

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Optimal placement of UAVs of an aerial mesh network in an emergency situation

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第1期12卷 343-358页

作者： Gupta, Manali Varma, Shirshu Indian Inst Informat Technol Allahabad Deptartment Informat Technol Prayagraj Uttar Pradesh India

There has been a rapid increase in usage of unmanned aerial vehicles (UAVs) in different application areas that are unfriendly to humans. These UAVs have been used in Aerial Mesh Networks (AMNs) that act as backbone network to support communication in a post-disaster scenario. However, there may be limited available number of UAV nodes that need to be utilized efficiently to improve the performance of such networks. Here, we consider three important objectives of the network i.e. target coverage, Quality of Service and energy consumption by the network that need to be optimized efficiently to improve the performance. Yet, it is a grueling task to optimize all of these conflicting objectives at the same time, which is affected by the height of UAVs. To optimize more than one conflicting objectives, we used metaheuristic based multi-objective optimization algorithms i.e. Multi-objective Particle Swarm Optimization (MOPSO), non-dominated sorting genetic algorithm ii (NSGA-ii), Strength Pareto Evolutionary algorithm 2 (SPEA2) and Pareto Envelope-based Selection algorithm ii (PESA-ii), which suggest the optimal placement of UAVs. These algorithms are compared based on four performance metrics i.e. generational distance, diversification metric, spread of non-dominant solutions and percentage of domination in three different scenarios. The rigorous experiments are performed by each algorithm in small, medium and large-scale scenarios to compare their results. The ANOVA's validation test suggests that SPEA2 performs better than others in small-scale scenarios while NSGA-ii performs better than others in medium and large-scale scenarios. However, MOPSO has lowest average execution time, after that NSGA-ii, then PESA-ii and then SPEA2.

关键词： Metaheuristic Multi-objective optimization Multi-objective Particle Swarm Optimization non-dominated sorting genetic algorithm ii Strength Pareto Evolutionary algorithm 2 Pareto Envelope-based Selection algorithm ii Optimal non-dominant solutions

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Weighted sum Pareto optimization of a three dimensional passenger vehicle suspension model using NSGA-ii for ride comfort and ride safety

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SMART STRUCTURES AND SYSTEMS 2018年第4期22卷 469-479页

作者： Bagheri, Mohammad Reza Mosayebi, Masoud Mandian, Asghar Keshavarzi, Ahmad Malek Ashtar Univ Technol Sch Mech Engn Shahinshahr Isfahan Iran Islamic Azad Univ Dept Mech Engn Khomeinishahr Branch Esfahan Iran

The present research study utilizes a multi-objective optimization method for Pareto optimization of an eight-degree of freedom full vehicle vibration model, adopting a non-dominated sorting genetic algorithm ii (NSGA-ii). In this research, a full set of ride comfort as well as ride safety parameters are considered as objective functions. These objective functions are divided in to two groups (ride comfort group and ride safety group) where the ones in one group are in conflict with those in the other. Also, in this research, a special optimizing technique and combinational method consisting of weighted sum method and Pareto optimization are applied to transform Pareto double-objective optimization to Pareto full-objective optimization which can simultaneously minimize all objectives. Using this technique, the full set of ride parameters of three dimensional vehicle model are minimizing simultaneously. In derived Pareto front, unique trade-off design points can selected which are non-dominated solutions of optimizing the weighted sum comfort parameters versus weighted sum safety parameters. The comparison of the obtained results with those reported in the literature, demonstrates the distinction and comprehensiveness of the results arrived in the present study.

关键词： full vehicle vibration model multi-objective optimization non-dominated sorting genetic algorithm ii passenger vehicle suspension model weighted sum Pareto optimization

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Modelling and multi-objective optimisation of the convective heat transfer characteristics and pressure drop of low concentration TiO₂-water nanofluids in the turbulent flow regime

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2013年 67卷 646-653页

作者： Mehrabi, M. Sharifpur, M. Meyer, J. P. Univ Pretoria Dept Mech & Aeronaut Engn ZA-0002 Pretoria South Africa

In the research for this paper, a GA-PNN hybrid system was used for modelling the convective heat transfer characteristics and pressure drop of TiO2-water a nanofluid in a fully developed turbulent flow based on an experimentally obtained train and test data set. Models were developed for the Nusselt number and the pressure drop of the nanofluid as a function of Reynolds and Prandtl numbers, nanofluid volume concentration and average nanoparticle diameter. The results of the proposed models were compared with experimental data and with existing correlations. The validity of the proposed models was benchmarked by using statistical criteria and NSGA-ii was used for multi-objective optimisation for the convective heat transfer. In the optimisation procedure model, the Nusselt number :and pressure drop were considered as the objective functions. However, when the set of decision variables was selected based on the Pareto set, it ensures the best possible combination of objectives. The Pareto front of multi-objective optimisation of the Nusselt number and pressure drop proposed models were also shown and discussed. It was found that application of the multi-objective optimisation method for the turbulent convective heat transfer characteristics and pressure drop of TiO2-water nanofluid could lead to finding the best design points based on the importance of the objective function in the design procedure. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： TiO2-water nanofluid Nusselt number Pressure drop genetic algorithm-polynomial neural network (GA-PNN) Group method of data handling (GMDH) Multi-objective optimisation non-dominated sorting genetic algorithm ii (NSGA-ii)

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A target recognition method for maritime surveillance radars based on hybrid ensemble selection

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2017年第15期48卷 3334-3345页

作者： Fan, Xueman Hu, Shengliang He, Jingbo Naval Univ Engn Shipborne Command & Control Dept Elect Engn Coll Wuhan Hubei Peoples R China

In order to improve the generalisation ability of the maritime surveillance radar, a novel ensemble selection technique, termed Optimisation and Dynamic Selection (ODS), is proposed. During the optimisation phase, the non-dominated sorting genetic algorithm ii for multi-objective optimisation is used to find the Pareto front, i.e. a set of ensembles of classifiers representing different tradeoffs between the classification error and diversity. During the dynamic selection phase, the meta-learning method is used to predict whether a candidate ensemble is competent enough to classify a query instance based on three different aspects, namely, feature space, decision space and the extent of consensus. The classification performance and time complexity of ODS are compared against nine other ensemble methods using a self-built full polarimetric high resolution range profile data-set. The experimental results clearly show the effectiveness of ODS. In addition, the influence of the selection of diversity measures is studied concurrently.

关键词： non-dominated sorting genetic algorithm ii meta-learning dynamic ensemble selection target recognition

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Applying a decision-making framework for resolving conflicts when selecting windows and blinds

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ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT 2019年第5期15卷 382-401页

作者： Jalilzadehazhari, Elaheh Johansson, J. Mahapatra, K. Linnaeus Univ Dept Forestry & Wood Technol Vaxjo Sweden Linnaeus Univ Dept Built Environm & Energy Technol Vaxjo Sweden

The selection of the most appropriate window and blind design is a challenging task due to the existence of potential conflicts between visual comfort, thermal comfort, energy consumption and life cycle cost. Resolving these conflicts relies on a trade-off window and blind design. This research applied a decision-making framework to select a trade-off window and blind design for an office room in Sweden. The decision-making framework was developed based on integrating the non-dominated sorting genetic algorithm-ii and the analytical hierarchy process. The first step in the application of the framework was to generate a model of the office room using EnergyPlus. Six types of window and four types of blind;an internal venetian blind, an internal roller curtain, an external venetian blind and an external overhang panel, were modelled in EnergyPlus. The second step was to run an optimisation using non-dominated sorting genetic algorithm-ii. For this purpose, various window and blind design variables were specified in modeFRONTIER platform. The third and last step in the application of the decision-making framework was to select a trade-off window and blind design using analytical hierarchy process. The results show the strength of the decision-making framework in selecting a trade-off design, and thereby the ability to resolve conflicts through intelligent use of simulation in analyzing big-data in built environment, energy and cost sectors. Since, the computation and processing power for performing simulations is constantly increasing, architects and designers can exploit the decision-making framework and locate a trade-off design in a relatively short period of time.

关键词： Decision-making big data optimisation non-dominated sorting genetic algorithm ii analytical hierarchy process trade-off design

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Study on performances of supercritical CO₂ recompression Brayton cycles with multi-objective optimization

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APPLIED THERMAL ENGINEERING 2017年 114卷 1335-1342页

作者： Deng, Q. H. Wang, D. Zhao, H. Huang, W. T. Shao, S. Feng, Z. P. Xi An Jiao Tong Univ Inst Turbomachinery Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China

Supercritical carbon dioxide recompression Brayton cycles (SCO2RBC) have the potential for higher practicability and better economy, because its high-efficiency, compact size, using standard construction materials, low-cost, and so on. Based on the mathematical model of thermodynamics, the thermodynamic parameters optimizing method for SCO2RBC by the non-dominated sorting genetic algorithm ii (NSGA-H) with exergy efficiency and net power output as its objective functions is presented in this paper. The parametric study shows that the cycle performance could be greatly affected by some key parameters. And the optimization gets the Pareto optimal relation curve, which indicates that they cannot reach the maximum at the same time and there is a conflicting relation between them. In addition, the relationship of the key cycle parameters is established and then a fast query system is built for SCO2RBC design based on artificial neural network (ANN). It shows that the predicted results by ANN are in substantial agreement with optimization results by NSGA-ii with a good accuracy and the designers could firstly determine the numerical value of the cycle net power output or the exergy efficiency as needed, and then the key cycle parameters could be retrieved by the query system fast and accurately. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Supercritical carbon dioxide recompression Brayton cycles Thermodynamic analysis Multi-objective optimization non-dominated sorting genetic algorithm ii Artificial neural network Fast query system

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APPLICATION OF THE HEADSTOCK OF CNC BORING MACHINE FOR TRACTOR ENGINE CYLINDER BLOCK BASED ON MULTI-OBJECTIVE genetic algorithm

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APPLIED ENGINEERING IN AGRICULTURE 2021年第2期37卷 343-349页

作者： Wang, Yahui Shi, Ling Dang, Yiqi Sun, Shengkai Zhang, Huipeng North China Univ Water Resources & Elect Power Sch Mech Engn Zhengzhou Henan Peoples R China State Grid Corp China Henan Elect Power Res Inst Zhengzhou Henan Peoples R China

The tractor is one of the most frequently used equipment in agricultural production, and its mass production is the general trend. With the continuous advancement of the global industrialization process, the importance of Computer Numerical Control (CNC) machine in the entire industrial production has become more and more prominent, and the application of CNC machine in tractor manufacturing has greatly improved production efficiency. This article takes the headstock of a single-sided horizontal CNC boring machine dedicated to processing tractor 6-cylinder engine cylinders as the research objective, takes the key parameters of the gear train in the headstock as the optimization design variables, constructs constraints, such as modulus, tooth width, etc., establishes a multi-objective optimization mathematical model, uses the non-dominated sorting genetic algorithm ii (NSGA-ii) to process the model and obtains the Pareto solution set through multiple iterations. The optimization results show that the volume, center distance and the reciprocal of coincidence degree of the main shaft 1 transmission group are reduced in varying degrees. Finally, it is compared with the weighted sum method and genetic algorithm (GA) to highlight the superiority of NSGA-ii.

关键词： Headstock Multi-optimization non-dominated sorting genetic algorithm ii Tractor cylinder

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