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Optimal capacitor placement in distribution networks regarding uncertainty in active power load and distributed generation units production

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第12期10卷 3060-3067页

作者： Jannat, Mohamed B. Savic, Aleksandar S. Univ Belgrade Sch Elect Engn Bulevar Kralja Aleksandra 73 Belgrade 11000 Serbia

The paper proposes a new method for optimal allocation of shunt capacitors in distribution network with renewable distributed generation units such as wind turbines and/or solar power plants. The proposed method, based on application of the Monte Carlo simulation methods, respects the uncertainties associated with load demand and renewable sources power production. The optimisation problem is modelled by two criterion functions with the aim of improving voltage profile in a network by optimal selection of the locations and installed powers of the shunt capacitors. The suggested criterion functions have been optimised simultaneously by applying the non-dominated sorting genetic algorithm. On the basis of the measured data on wind speed and solar radiation, the method has been tested by the example of a real distribution network.

关键词： Monte Carlo methods genetic algorithms distributed power generation distribution networks optimal capacitor placement distribution networks active power load renewable distributed generation units wind turbines solar power plants Monte Carlo simulation methods load demand renewable sources power production optimisation problem criterion functions voltage profile nondominated sorting genetic algorithm wind speed solar radiation

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Multi-objective hybrid estimation of distribution algorithm-interior point method-based meter placement for active distribution state estimation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第3期12卷 767-779页

作者： Prasad, Sachidananda Mallesham, Vinod Kumar Dulla Natl Inst Technol Warangal Dept Elect Engn Warangal 506004 Telangana State India

This study proposes a new multi-objective hybrid estimation of distribution algorithm (EDA)-interior point method (IPM) algorithm to obtain the optimal location of measuring devices for state estimation (SE) in active distribution networks. The objective functions to be minimised are, the total network configuration cost, the average relative percentage error of bus voltage magnitude and angle estimates. As the objectives are conflicting in nature, a multi-objective Pareto-based non-dominated sorting EDA has been proposed in this study. Moreover, due to poor exploitation capability of the EDA, it is hybridised with IPM to improve its local searching ability in the search space. The hybridisation of EDA and IPM brings a higher degree of balance between the exploration and exploitation capability of the algorithm during the search process. Furthermore, the loads and generators are treated as stochastic variable and the impact of different type of distributed generations on SE performance has also been investigated. The efficiency of the proposed algorithm is tested on PG&E 69-bus system and Indian 85-bus radial distribution network. The obtained results are compared with conventional EDA, particle swarm optimisation, non-dominated sorting genetic algorithm and also with existing techniques in the literature such as dynamic programming and ordinal optimisation algorithm.

关键词： power distribution economics costing Pareto optimisation distributed power generation particle swarm optimisation genetic algorithms dynamic programming multiobjective hybrid estimation distribution algorithm-interior point method meter placement active distribution state estimation EDA-IPM algorithm measuring devices active distribution networks total network configuration cost average relative percentage error bus voltage magnitude angle estimates multiobjective Pareto-based nondominated sorting EDA local searching ability search process stochastic variable distributed generations SE performance PG& E 69-bus system 85-bus radial distribution network particle swarm optimisation nondominated sorting genetic algorithm dynamic programming ordinal optimisation algorithm

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Skip-stop operation plan for urban rail transit considering bounded rationality of passengers

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IET INTELLIGENT TRANSPORT SYSTEMS 2022年第1期16卷 24-40页

作者： Cheng, Linna Zhu, Changfeng Wang, Qingrong Wang, Wenxian Zhang, Zhengkun Sun, Wenjun Wuyi Univ Sch Rail Transportat Wuyi Guangdong Peoples R China Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou Gansu Peoples R China Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Gansu Peoples R China

Reasonably optimizing the urban rail transit stop plan from the perspective of bounded rational cognition of passengers regarding their travel choices is an important way to improve riding environment, transportation capacity, and service quality. Based on the analysis of the characteristics of passengers' bounded rational choice behaviour, starting from two aspects of travel time and congestion, the prospect theory is applied to describe optimization goals: maximum travel time savings and maximum congestion costs savings. Subsequently, an urban rail transit stop plan optimization model is constructed, and a non-dominated sorting genetic algorithm with the elite strategy is designed to solve the model, and stop plans based on the calculated Pareto solution set are selected. A case study of an urban rail transit with 13 station nodes verifies the research conclusion as follows: compared with the traditional station stopping plan, the skip-stop plan has better passenger flow adaptability, in which the average travel time of passengers is shortened by 22.57 min at most, and the congestion degree is reduced by 10.93% at most. Based on this, the impact of different factors on the stop plan, such as the target weight, passenger flow, and behavioural parameters, is further discussed.

关键词： transportation travel choices bounded rational cognition Pareto solution set urban rail transit stop plan optimization model maximum travel time savings maximum congestion costs savings Pareto optimisation bounded rational choice behaviour nondominated sorting genetic algorithm skip-stop operation plan passenger flow adaptability average travel time station stopping plan rail traffic genetic algorithms

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Multi-objective expansion planning approach: distant wind farms and limited energy resources integration

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IET RENEWABLE POWER GENERATION 2013年第6期7卷 652-668页

作者： Javadi, Mohammad Sadegh Saniei, Mohsen Mashhadi, Habib Rajabi Gutierrez-Alcaraz, Guillermo Shahid Chamran Univ Ahvaz Dept Elect Engn Ahvaz Iran Ferdowsi Univ Mashhad Dept Elect Engn Mashhad Iran Inst Tecnol Morelia Dept Elect Engn Morelia Michoacan Mexico

This study presents a multi-objective framework to evaluate the integration of distant wind farms with associated transmission network upgrades on optimal power system planning. The presented approach also extends the technique to include the consideration of energy limitations associated with the installed hydro generation facilities. This study attempts to emphasise on the reliability implications rather than the production cost evaluation aspects. The decision making is based on hierarchal level II (HL-II) Expected Energy Not Served as an entire power system reliability assurance, and capital cost plus annual operational cost as an economical index. Non-dominated sorting genetic algorithm is adopted to achieve the Pareto front of the aforementioned multi-objective problem. A fuzzy satisfying method, designated as the distance metric, is used to represents a trade-off between different objectives. To numerically evaluate the efficiency of the proposed method, simulation results on three case studies are provided. In spite of huge computation burden at HL-II reliability assessment, the results indicate high efficiency of the proposed method.

关键词： fuzzy set theory genetic algorithms hydroelectric power stations power generation economics power generation planning power generation reliability power transmission economics power transmission planning power transmission reliability resource allocation wind power plants multiobjective expansion planning approach distant wind farms limited energy resources integration distant wind farms integration associated transmission network optimal power system planning energy limitations installed hydrogeneration facilities reliability implications production cost evaluation aspects decision making hierarchal level II expected energy power system reliability assurance capital cost annual operational cost economical index nondominated sorting genetic algorithm Pareto front fuzzy satisfying method distance metric HL-II reliability assessment

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Pareto Optimal Synthesis of the Linear Array Geometry for Minimum Side lobe Level and Null Control During Beam Scanning

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2010年第5期20卷 557-566页

作者： Gunes, F. Tokan, F. Yildiz Tech Univ Dept Elect & Commun Engn Fac Elect & Elect TR-34349 Istanbul Turkey

In this work, synthesis of the linear array geometry is put forward as a constrained vector optimization problem whose components are to meet the minimum sidelobe level (SU) and control of the wide/narrow null placement during beam scanning whose range can vary from zero to the wide bands. As these synthesis objectives generally conflict with each other, nondominated solutions are acquired using the nondominated sorting genetic algorithm- II (NSGA-II) as a fast nondominated genetic sorting algorithm. Then, the Pareto frontiers are obtained using these trade-off solution sets between the maximum SLI,, null control, and scanning range to provide a view of all design options. Thus, the pattern features resulted from these Pardo frontiers are valid for any chosen main beam direction within its full prescribed beam scanning range. The same Pareto optimal synthesis procedure can be applied to a thinned linear antenna array. A thinned linear antenna array is obtained by a simple genetic optimization by rounding the excitation amplitudes either to I or 0 to minimize the maximum of side lobe level (MSLL) during the beam scanning within the prescribed region as stated in the multiobjective function. Finally, some typical Pardo optimal radiation patterns of the scanning arrays are synthesized with only perturbating the positions of the array elements, and their full electromagnetic wave simulations are also completed to examine the resulted mutual coupling effects between the elements of the arrays. It can be concluded that the Pareto optimal synthesis procedure gives the scanning linear antenna arrays with successful radiation performance. (C) 2010 Wiley Periodicals, Inc Int J RF and Microwave CAE 20 557-566.2010

关键词： linear array sidelobe suppression. null control beam scanmilit constrained multiobjective optimization pareto-optimal solutions nondominated sorting genetic algorithm

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Resource management of opportunistic digital array radar antenna aperture for pattern synthesis

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IET RADAR SONAR AND NAVIGATION 2017年第5期11卷 829-837页

作者： Han, Qinghua Pan, Minghai Gong, Shufeng Long, Weijun Nanjing Univ Aeronaut & Astronaut Key Lab Radar Imaging & Microwave Photon Minist Educ Nanjing 211106 Jiangsu Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310014 Zhejiang Peoples R China Nanjing Res Inst Elect Technol Nanjing 210039 Jiangsu Peoples R China

Due to the complex time-varying environments and the unknown target information, the uncertainty of target information is introduced into the echo signal. Owing to the uncertainty of target information, the number and operating modes of antenna array elements allocated for pattern synthesis are uncertain. Hence to reduce the influence of uncertainty of target information and save the antenna array elements, in the light of the uncertainty of the number and the distribution of the array elements in working state, a novel fuzzy random chance-constrained programming model of opportunistic digital array radar antenna aperture resource management is proposed. Moreover, this model can determine the information of radar target from the echo signal by reasonable resource management and allocation under the condition that the specified confidence levels are satisfied. Meanwhile, for solving the multi-object model, the fuzzy random simulation is integrated into fast and elitist non-dominated sorting genetic algorithm (NSGA_II) to compose a hybrid intelligent optimisation algorithm. Finally, two simulations are carried out to verify the validity of this model.

关键词： radar antennas antenna arrays antenna radiation patterns echo fuzzy set theory genetic algorithms opportunistic digital array radar antenna aperture pattern synthesis resource management complex time-varying environments echo signal fuzzy random chance-constrained programming model nondominated sorting genetic algorithm NSGA_II hybrid intelligent optimisation algorithm

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Application of NSGA-II algorithm to Generation Expansion Planning

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IEEE TRANSACTIONS ON POWER SYSTEMS 2009年第1期24卷 454-461页

作者： Kannan, S. Baskar, S. McCalley, James D. Murugan, P. Arulmigu Kalasalingam Coll Engn Dept Elect & Elect Engn Krishnankoil 626190 Tamil Nadu India Thiagarajar Coll Engn Dept Elect Engn Madurai 625015 Tamil Nadu India Iowa State Univ Dept Elect & Comp Engn Ames IA 50010 USA Arulmigu Kalasalingam Coll Engn Elect & Commun Engn Dept Krishnankoil 626190 Tamil Nadu India

This paper describes use of a multiobjective optimization method, elitist nondominated sorting genetic algorithm version II (NSGA-II), to the generation expansion planning (GEP) problem. The proposed model provides for decision maker choice from among the different trade-off solutions. Two different problem formulations are considered. In one formulation, the first objective is to minimize cost;the second objective is to minimize sum of normalized constraint violations. In the other formulation, the first objective is to minimize investment cost;the second objective is to minimize outage cost (or maximize reliability). Virtual mapping procedure is introduced to improve the performance of NSGA-II. The GEP problem considered is a test system for a six-year planning horizon having five types of candidate units. The results are compared and validated.

关键词： Generation expansion planning multiobjective nondominated sorting genetic algorithm optimization virtual mapping procedure

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Multi-objective optimization of a diesel particulate filter: an acoustic approach

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PARTICULATE SCIENCE AND TECHNOLOGY 2022年第4期40卷 465-474页

作者： Ozturk, Sinem Erol, Haluk Istanbul Tech Univ Mech Engn Fac TR-34437 Istanbul Turkey

In order to decrease the soot particles' harmful emission, it is necessary to use a diesel particulate filter (DPF) on cars. Recent years, many countries standardize using DPF on cars. In this paper, a multi-objective design optimization study is performed by using the nondominated sorting genetic algorithm (NSGA-II) to obtain an optimum DPF geometry. In the present study, two objective functions were determined to obtain an optimum DPF. The first was to maximize sound transmission loss (TL) values to increase the acoustical properties of the DPF, while the second was to minimize the back pressure to cause the diesel engine to work efficiently and consume less energy. The optimization problem has been constructed in the form of maximizing the sum of the values of TL at all frequencies, as optimizing the problem for each frequency separately means a new DPF at each frequency, which has a different geometry. In the literature, optimization of the DPF has been carried out for a few parameters by just changing one parameter value and keeping others stable. However, in this paper, optimization of the acoustic performance of DPFs was performed for more than one parameter in the range of their specific values simultaneously.

关键词： Diesel particulate filter multi-objective optimization nondominated sorting genetic algorithm sound transmission loss pressure drop

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Performance enhancement of a VCO using symbolic modelling and optimisation

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IET CIRCUITS DEVICES & SYSTEMS 2018年第2期12卷 196-202页

作者： Panda, Madhusmita Patnaik, Santosh Kumar Mal, Ashis Kumar NIT Dept Elect & Commun Engn Durgapur India NIST Dept Elect & Commun Engn Berhampur Orissa India

This study proposed an application of symbolic technique on the characterisation of a ring voltage controlled oscillator (VCO) for optimum performance. Here nullor-based symbolic noise modelling and analysis of the CMOS ring VCO is carried out. Circuit equations are processed through modelling of all the metal-oxide-semiconductor field-effect transistors with their nullor equivalent. The closed-form expressions for the total output noise density and phase noise of VCO are obtained. With this technique, the total output noise density and phase noise calculated are compared with the results obtained from the transistor-level simulation. From the comparison, it has been observed that the nullor-based modelling and analysis simplifies the noise expression as well as reduces CPU execution time. The performance enhancement of the VCO is carried out using two different optimisation techniques. These are particle swam optimisation and non-dominated sorting genetic algorithm. The results obtained for the optimised VCO are then tested using SPICE. The SPICE result shows a significant improvement in phase noise, power consumption and tuning range for the optimised VCO.

关键词： voltage-controlled oscillators CMOS analogue integrated circuits integrated circuit modelling phase noise integrated circuit noise particle swarm optimisation genetic algorithms symbolic modelling ring voltage controlled oscillator ring VCO nullor-based symbolic noise modelling CMOS analysis circuit equation metal-oxide-semiconductor field-effect transistors nullor equivalent total output noise density phase noise transistor-level simulation particle swam optimisation nondominated sorting genetic algorithm SPICE

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A multi-objective evolutionary approach for planning and optimal condition restoration of secondary distribution networks

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APPLIED SOFT COMPUTING 2020年 90卷 106182-000页

作者： Aviles, J. P. Mayo-Maldonado, J. C. Micheloud, O. Tecnol Monterrey Monterrey Mexico

A secondary distribution network (SDN), corresponding to the final user low voltage distribution circuit, is continuously growing due to a persistent increase in load demand. Consequently, the performance of any optimized design will inevitably degrade over time. To avoid the associated repercussions such as faults, congestion, voltage drops, and other major quality issues, we are eventually prompted to redesign this part of the grid. To do so, we propose a Two-Stage Multi-Objective Evolutionary Approach (TS-MOEAP), which is able to find a new optimal network configuration, circumventing the associated quality issues. The proposed approach is oriented to improve the performance of SDNs by combining the concepts of network reconfiguration (NR) and optimal placement of distribution transformers (DTs). Due to the large and complex topology of SDNs, we deal with a hard combinatorial, non-convex, and nonlinear optimization problem. Consequently, to facilitate the resolution of the problem, the proposal is divided into two stages: (1) optimal placement and sizing of distribution transformers, as well as conductor sizing and branch routing, and (2) optimal network reconfiguration. For the first stage, an improved particle swarm optimization technique (IPSO) combined with a greedy algorithm is used, and for the second stage, an improved nondominated sorting genetic algorithm with a heuristic mutation operator (NSGA-HO) is implemented. The approach redesigns SDNs by minimizing total power loss and investment costs while satisfying quality issues and technical constraints. The proposed approach is validated by improving a real-life SDN with critical quality and technical issues. We also compare the results with respect to other state-of-the-art algorithms. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Distribution network planning Distribution network reconfiguration Multi-objective optimization nondominated sorting genetic algorithm Particle swarm optimization

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