Sensor Optimal Placement for Bridge Structure Based on Single Parents Genetic Algorithm with Different Fitness Functions

2010 ◽  
Vol 456 ◽  
pp. 115-124 ◽  
Author(s):  
Han Bing Liu ◽  
Chun Li Wu ◽  
Jing Wang
Keyword(s):  
Genetic Algorithm ◽  
Linear Independence ◽  
Single Parents ◽  
Optimal Placement ◽  
Bridge Structure ◽  
Step Method ◽  
Arch Bridge ◽  
Large Span ◽  
Fitness Functions ◽  
Sensor Position

An investigation into sensor optimal placement for bridge structure based on Single Parents Genetic Algorithm with different fitness functions has been carried on. Five fitness functions are designed from several aspects on linear independence, orthogonality and energy of mode. The two-step method is proposed to determine the number of sensors firstly and then sensor position. An example of a large span arch bridge has proved the following facts: the Single Parents Genetic Algorithm is quite suitable to sensor optimal placement for bridge structure. Fitness functions designed by effective independence index and MAC and BHM combined index are more desirable than other evaluation indices. Two-step method used to determine the number of sensors and position is very effective.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

scholarly journals Optimal Sizing and Placement of Capacitor Banks in Distribution Networks Using a Genetic Algorithm

Electricity ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 187-204
Author(s):  
Gian Giuseppe Soma
Keyword(s):  
Genetic Algorithm ◽  
Electricity Market ◽  
Distribution Systems ◽  
Reactive Power ◽  
Electricity Consumption ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Capacitor Banks ◽  
Control Pattern ◽  
Definition Of

Nowadays, response to electricity consumption growth is mainly supported by efficiency; therefore, this is the new main goal in the development of electric distribution networks, which must fully comply with the system’s constraints. In recent decades, the issue of independent reactive power services, including the optimal placement of capacitors in the grid due to the restructuring of the electricity industry and the creation of a competitive electricity market, has received attention from related companies. In this context, a genetic algorithm is proposed for optimal planning of capacitor banks. A case study derived from a real network, considering the application of suitable daily profiles for loads and generators, to obtain a better representation of the electrical conditions, is discussed in the present paper. The results confirmed that some placement solutions can be obtained with a good compromise between costs and benefits; the adopted benefits are energy losses and power factor infringements, taking into account the network technical limits. The feasibility and effectiveness of the proposed algorithm for optimal placement and sizing of capacitor banks in distribution systems, with the definition of a suitable control pattern, have been proved.

scholarly journals Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 172
Author(s):  
Sunny Katyara ◽  
Muhammad Fawad Shaikh ◽  
Shoaib Shaikh ◽  
Zahid Hussain Khand ◽  
Lukasz Staszewski ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Fuzzy Inference System ◽  
Distribution System ◽  
Fuzzy Inference ◽  
Management Strategies ◽  
Test System ◽  
Optimal Placement ◽  
Inference System ◽  
Simulated Environment

With the rising load demand and power losses, the equipment in the utility network often operates close to its marginal limits, creating a dire need for the installation of new Distributed Generators (DGs). Their proper placement is one of the prerequisites for fully achieving the benefits; otherwise, this may result in the worsening of their performance. This could even lead to further deterioration if an effective Energy Management System (EMS) is not installed. Firstly, addressing these issues, this research exploits a Genetic Algorithm (GA) for the proper placement of new DGs in a distribution system. This approach is based on the system losses, voltage profiles, and phase angle jump variations. Secondly, the energy management models are designed using a fuzzy inference system. The models are then analyzed under heavy loading and fault conditions. This research is conducted on a six bus radial test system in a simulated environment together with a real-time Power Hardware-In-the-Loop (PHIL) setup. It is concluded that the optimal placement of a 3.33 MVA synchronous DG is near the load center, and the robustness of the proposed EMS is proven by mitigating the distinct contingencies within the approximately 2.5 cycles of the operating period.

Optimal Placement of Horizontal- and Vertical-Axis Wind Turbines in a Wind Farm for Maximum Power Generation Using a Genetic Algorithm

2011 ◽  
Author(s):  
Xiaomin Chen ◽  
Ramesh Agarwal
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbines ◽  
Wind Farm ◽  
Optimization Problem ◽  
Vertical Axis ◽  
Optimal Placement ◽  
Vertical Axis Wind Turbines ◽  
Wake Effects ◽  
Horizontal Axis Wind Turbines ◽  
Wake Model

In this paper, we consider the Wind Farm layout optimization problem using a genetic algorithm. Both the Horizontal–Axis Wind Turbines (HAWT) and Vertical-Axis Wind Turbines (VAWT) are considered. The goal of the optimization problem is to optimally place the turbines within the wind farm such that the wake effects are minimized and the power production is maximized. The reasonably accurate modeling of the turbine wake is critical in determination of the optimal layout of the turbines and the power generated. For HAWT, two wake models are considered; both are found to give similar answers. For VAWT, a very simple wake model is employed.

Optimal placement of dampers in structures using genetic algorithm

2006 ◽  
Vol 23 (6) ◽  
pp. 597-606 ◽  
Author(s):  
Hamid Movaffaghi ◽  
Olof Friberg
Keyword(s):  
Genetic Algorithm ◽  
Optimal Placement

Construction Monitoring Technology Research on Large-Span V Structure Tied Arch Bridge

2013 ◽  
Vol 351-352 ◽  
pp. 1240-1243 ◽  
Author(s):  
Ting Yin ◽  
Wei Zhang ◽  
Yan Bo Zhao ◽  
Xiao Long Sun
Keyword(s):  
Actual State ◽  
Construction Process ◽  
Bridge Construction ◽  
Monitoring Point ◽  
Monitoring Technology ◽  
Construction Monitoring ◽  
Arch Bridge ◽  
Large Span ◽  
Long Span ◽  
Tied Arch Bridge

Large-span V structure tied arch bridge structure system is complex. In order to make the actual state of the bridge into the ideal state of maximum close to the design, we must make real-time monitoring in the key parts of the structure, and adjust to the design of the data by analysis calculated, to ensure the safety and reliability of arch bridge in the course of construction and into stress after the completed bridge. Combined with the construction monitoring of the main Xinglong Bridge on Si River, this paper introduces the main bridge construction process measurement a preliminary study on the large span V configuration basket tied arch bridge construction monitoring technology. Based on the construction monitoring of Xinglong Bridge on Si River, this paper introduces the main contents and methods of monitoring point location, monitoring control of construction process, and discussed the construction monitoring technology of long-span X-style tied arch bridge V structure.

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