Study on Coordinated Control of Multi-Distribution Static Var Compensator

2014 ◽  
Vol 672-674 ◽  
pp. 1127-1131
Author(s):  
Ming Jiang Zhang ◽  
Xi Lin Zhang ◽  
Zhen Hao Wang ◽  
Ling Wang
Keyword(s):  
Genetic Algorithm ◽  
Power Quality ◽  
Distribution System ◽  
Optimization Problem ◽  
Coordinated Control ◽  
Parameters Optimization ◽  
Static Var Compensator ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Simulation Results

DFACTS devices can synthetically manage power quality problems. As one of the most DFACTS devices, the coordinated control of multi-distribution static var compensator should be considered. Controllers are separately designed aiming at different functions, that means the controllers are isolated even contradictory. In allusion to the problem that the separately designed DFACTS controllers exist interactions, the paper turns the coordination of the DFACTS controller into multi-objective optimization problem, takes the single-load infinite-bus distribution system with two DSVC as the research object, using the Non-dominated Sorting Genetic Algorithm with elitism approach (NSGA-II) for DFACTS controller parameters optimization, and the simulation results show the effectiveness of the algorithm.

scholarly journals Multi-objective optimization of retaining wall using genetic algorithm

2021 ◽  
Vol 8 (1-2) ◽  
pp. 58-65
Author(s):  
Filip Dodigović ◽  
Krešo Ivandić ◽  
Jasmin Jug ◽  
Krešimir Agnezović
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Retaining Wall ◽  
Silty Sand ◽  
Embedment Depth ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Definition Of ◽  
The Cost

The paper investigates the possibility of applying the genetic algorithm NSGA-II to optimize a reinforced concrete retaining wall embedded in saturated silty sand. Multi-objective constrained optimization was performed to minimize the cost, while maximizing the overdesign factors (ODF) against sliding, overturning, and soil bearing resistance. For a given change in ground elevation of 5.0 m, the width of the foundation and the embedment depth were optimized. Comparing the algorithm's performance in the cases of two-objective and three objective optimizations showed that the number of objectives significantly affects its convergence rate. It was also found that the verification of the wall against the sliding yields a lower ODF value than verifications against overturning and soil bearing capacity. Because of that, it is possible to exclude them from the definition of optimization problem. The application of the NSGA-II algorithm has been demonstrated to be an effective tool for determining the set of optimal retaining wall designs.

Multi-Objective Evolutionary Algorithms

2016 ◽  
pp. 301-345
Author(s):  
A. K. Nandi ◽  
K. Deb
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Optimization Problem ◽  
Performance Metrics ◽  
Research Work ◽  
Primary Objective ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Mould Material ◽  
Multi Objective

The primary objective in designing appropriate particle reinforced polyurethane composite which will be used as a mould material in soft tooling process is to minimize the cycle time of soft tooling process by providing faster cooling rate during solidification of wax/plastic component. This chapter exemplifies an effective approach to design particle reinforced mould materials by solving the inherent multi-objective optimization problem associated with soft tooling process using evolutionary algorithms. In this chapter, first a brief introduction of multi-objective optimization problem with the key issues is presented. Then, after a short overview on the working procedure of genetic algorithm, a well- established multi-objective evolutionary algorithm, namely NSGA-II along with various performance metrics are described. The inherent multi-objective problem in soft tooling process is demonstrated and subsequently solved using an elitist non-dominated sorting genetic algorithm, NSGA-II. Multi-objective optimization results obtained using NSGA-II are analyzed statistically and validated with real industrial application. Finally the fundamental results of this approach are summarized and various perspectives to the industries along with scopes for future research work are pointed out.

Multi-Objective Optimization of Internal Combustion Engine Valve Springs Based on NSGA-II

2014 ◽  
Vol 945-949 ◽  
pp. 473-477
Author(s):  
You Jian Wang ◽  
Guang Zhang
Keyword(s):  
Genetic Algorithm ◽  
Internal Combustion Engine ◽  
Optimization Problem ◽  
Combustion Engine ◽  
Graphical Methods ◽  
Multi Objective Optimization ◽  
Design Quality ◽  
Nsga Ii ◽  
Engine Valve ◽  
Multi Objective

The design of engine valve spring generally belongs to multi-objective optimum design. The traditional trying means and the graphical methods are difficult to solve the multi-objective optimization problem, and the traditional multi-objective algorithms have certain defects. The elitist non-dominated sorting genetic algorithm (NSGA-II) is an excellent multi-objective algorithm, which is widely used to solve problems of multi-objective optimization. This method can improve the design quality and efficiency, and it has much more engineering practical value.

Multi-Objective Evolutionary Algorithms

2017 ◽  
pp. 185-229
Author(s):  
A. K. Nandi ◽  
K. Deb
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Optimization Problem ◽  
Performance Metrics ◽  
Research Work ◽  
Primary Objective ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Mould Material ◽  
Multi Objective

The primary objective in designing appropriate particle reinforced polyurethane composite which will be used as a mould material in soft tooling process is to minimize the cycle time of soft tooling process by providing faster cooling rate during solidification of wax/plastic component. This chapter exemplifies an effective approach to design particle reinforced mould materials by solving the inherent multi-objective optimization problem associated with soft tooling process using evolutionary algorithms. In this chapter, first a brief introduction of multi-objective optimization problem with the key issues is presented. Then, after a short overview on the working procedure of genetic algorithm, a well- established multi-objective evolutionary algorithm, namely NSGA-II along with various performance metrics are described. The inherent multi-objective problem in soft tooling process is demonstrated and subsequently solved using an elitist non-dominated sorting genetic algorithm, NSGA-II. Multi-objective optimization results obtained using NSGA-II are analyzed statistically and validated with real industrial application. Finally the fundamental results of this approach are summarized and various perspectives to the industries along with scopes for future research work are pointed out.

Multi-Objective Optimization of a Piezoelectric Sandwich Ultrasonic Transducer by Using Elitist Non-Dominated Sorting Genetic Algorithm

2011 ◽  
Vol 474-476 ◽  
pp. 1808-1812
Author(s):  
Bo Fu ◽  
Yi Jing ◽  
Xuan Fu ◽  
Tobias Hemsel
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Ultrasonic Transducer ◽  
Analytical Models ◽  
Continuous Variables ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

The multi-objective optimal design of a piezoelectric sandwich ultrasonic transducer is studied. The maximum vibration amplitude and the minimum electrical input power are considered as optimization objectives. Design variables involve continuous variables (dimensions of the transducer) and discrete variables (material types). Based on analytical models, the optimal design is formulated as a constrained multi-objective optimization problem. The optimization problem is then solved by using the elitist non-dominated sorting genetic algorithm (NSGA-II) and Pareto-optimal designs are obtained. The optimized results are analyzed and the preferred design is proposed. The optimization procedure presented in this contribution can be applied in multi-objective optimization problems of other piezoelectric transducers.

Multi-Objective Optimization Calibration of Spark Advanced Angle Based on NSGA-II Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 1959-1962
Author(s):  
Hao Ba ◽  
Bao Mei Qiu ◽  
Pei Pei Chen
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Gasoline Engine ◽  
Optimal Solution ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Improved Genetic Algorithm ◽  
Nsga Ii ◽  
Multi Objective ◽  
Optimal Solution Set

Modern gasoline engine spark advanced angle calibration is a multi-objective optimization problem, commonly used genetic algorithm to solve this problem. However, the traditional genetic algorithm tends to local optimum probability of a larger, easy to fall into premature, this defect is likely to cause the solution is not the optimal solution set. To address this issue, the non-dominated sorting genetic algorithm II for the spark advanced angle optimization, through crowding distance maintain the diversity, overcome super individuals overgrowth, improved genetic algorithm post search results. Experimental results show the effectiveness of this method.

scholarly journals Economic Environmental Dispatch of Wind Integrated Thermal Power System

2020 ◽  
Vol 8 (6) ◽  
pp. 5186-5192
Keyword(s):  
Genetic Algorithm ◽  
Power Plant ◽  
Thermal Power ◽  
Nsga Ii ◽  
Strength Pareto Evolutionary Algorithm ◽  
Experimental Constraint ◽  
Thermal Power System ◽  
Power Plant Operation ◽  
Simulation Results ◽  
The Given

In electric power plant operation, Economic Environmental Dispatch (EED) of a thermal-wind is a significant chore to involve allocation of production amongst the running units so the price, NOx extraction status and SO2 extraction status are enhanced concurrently whilst gratifying each and every experimental constraint. This is an exceedingly controlled multiobjective optimizing issue concerning contradictory objectives having Primary and Secondary constraints. For the given work, a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is recommended for taking care of EED issue. In simulation results that are obtained by applying the two test systems on the proposed scheme have been evaluated against Strength Pareto Evolutionary Algorithm 2 (SPEA 2).

Kinematic Multi-Objective Optimization of Circular-Toothed Gerotor Pumps by Genetic Algorithm

2017 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Design Method ◽  
Third Party ◽  
Low Flow ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Multi Objective ◽  
Pareto Curve

A gerotor gear generation algorithm has been developed that evaluates key performance objective functions to be minimized or maximized, and then an optimization algorithm is applied to determine the best design. Because of their popularity, circular-toothed gerotors are the focus of this study, and future work can extend this procedure to other gear forms. Parametric equations defining the circular-toothed gear set have been derived and implemented. Two objective functions were used in this kinematic optimization: maximize the ratio of displacement to pump radius, which is a measure of compactness, and minimize the kinematic flow ripple, which can have a negative effect on system dynamics and could be a major source of noise. Designs were constrained to ensure drivability, so the need for additional synchronization gearing is eliminated. The NSGA-II genetic algorithm was then applied to the gear generation algorithm in modeFRONTIER, a commercial software that integrates multi-objective optimization with third-party engineering software. A clear Pareto front was identified, and a multi-criteria decision-making genetic algorithm was used to select three optimal designs with varying priorities of compactness vs low flow variation. In addition, three pumps used in industry were scaled and evaluated with the gear generation algorithm for comparison. The scaled industry pumps were all close to the Pareto curve, but the optimized designs offer a slight kinematic advantage, which demonstrates the usefulness of the proposed gerotor design method.

Multi-Objective Optimal Load Distribution Based on Sub Goals Multiplication and Division in Power Plants

2014 ◽  
Vol 494-495 ◽  
pp. 1715-1718
Author(s):  
Gui Li Yuan ◽  
Tong Yu ◽  
Juan Du
Keyword(s):  
Genetic Algorithm ◽  
Load Distribution ◽  
Power Plants ◽  
Optimization Problem ◽  
Thermal Power ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimal Load ◽  
Optimal Load Distribution

The classic multi-objective optimization method of sub goals multiplication and division theory is applied to solve optimal load distribution problem in thermal power plants. A multi-objective optimization model is built which comprehensively reflects the economy, environmental protection and speediness. The proposed model effectively avoids the target normalization and weights determination existing in the process of changing the multi-objective optimization problem into a single objective optimization problem. Since genetic algorithm (GA) has the drawback of falling into local optimum, adaptive immune vaccines algorithm (AIVA) is applied to optimize the constructed model and the results are compared with that optimized by genetic algorithm. Simulation shows this method can complete multi-objective optimal load distribution quickly and efficiently.

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