Implementation of Genetic Algorithms in PV Modules Power Optimization: Simulations and Experimental Results

2012 ◽  
Author(s):  
Despoina I. Makrygiorgou ◽  
Eleftheria C. Pyrgioti ◽  
Antonio T. Alexandridis
Keyword(s):  
Genetic Algorithms ◽  
Power Optimization ◽  
Experimental Results ◽  
Pv Modules

Retrieving Assembly Part Design Using Case-Based Reasoning and Genetic Algorithms

2005 ◽  
Author(s):  
Guanghsu A. Chang ◽  
Cheng-Chung Su ◽  
John W. Priest
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Similarity Measures ◽  
Experimental Results ◽  
Case Based Reasoning ◽  
Feature Weight ◽  
Similar Part ◽  
Case Base ◽  
Case Based

Artificial intelligence (AI) approaches have been successfully applied to many fields. Among the numerous AI approaches, Case-Based Reasoning (CBR) is an approach that mainly focuses on the reuse of knowledge and experience. However, little work is done on applications of CBR to improve assembly part design. Similarity measures and the weight of different features are crucial in determining the accuracy of retrieving cases from the case base. To develop the weight of part features and retrieve a similar part design, the research proposes using Genetic Algorithms (GAs) to learn the optimum feature weight and employing nearest-neighbor technique to measure the similarity of assembly part design. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.

Actuator hysteresis modeling and compensation with an adaptive search space based genetic algorithm

2021 ◽  
Author(s):  
Che-Hang Cliff Chan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Parameters Identification ◽  
Experimental Results ◽  
Adaptive Search ◽  
Real Coded Genetic Algorithm ◽  
Hysteresis Modeling ◽  
Convergence Performance ◽  
Solution Accuracy

The thesis presents a Genetic Algorithm with Adaptive Search Space (GAASS) proposed to improve both convergence performance and solution accuracy of traditional Genetic Algorithms(GAs). The propsed GAASS method has bee hybridized to a real-coded genetic algorithm to perform hysteresis parameters identification and hystereis invers compensation of an electromechanical-valve acuator installed on a pneumatic system. The experimental results have demonstrated the supreme performance of the proposed GAASS in the search of optimum solutions.

Research on Reactive Power Optimization Based on Genetic Algorithms in Distribution Network

2014 ◽  
Vol 644-650 ◽  
pp. 2476-2478
Author(s):  
Lin Yuan Wang
Keyword(s):  
Genetic Algorithms ◽  
Mutation Rate ◽  
Exponential Function ◽  
Reactive Power ◽  
Power Optimization ◽  
Distribution Network ◽  
Adaptive Mutation ◽  
Initial Population ◽  
Reactive Power Optimization ◽  
Slow Convergence

The reactive power optimization is formulated based on genetic algorithms in distribution net work. SGA has defects of slow convergence and being prone to immature convergence. In order to eliminate the defects, an improved GA is proposed in this thesis. CIP scheme is presented, which can guarantee diversity of the population by designing the initial population to obtain all the values within the definition area. A parameter called individual distributing degree is defined to describe how individuals are distributed in the definition area. Adaptive mutation rate is defined as an exponential function of the retained generations of the Elitism, and it is in inverse proportion to individual distribution degree. It accelerates the convergent process.

GENETIC ALGORITHMS FOR POLYGONAL APPROXIMATION OF DIGITAL CURVES

1999 ◽  
Vol 13 (07) ◽  
pp. 1061-1082 ◽  
Author(s):  
PENG-YENG YIN
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Learning Strategy ◽  
Approximation Error ◽  
Experimental Results ◽  
Polygonal Approximation ◽  
The Third ◽  
Digital Curve

In this paper, three polygonal approximation approaches using genetic algorithms are proposed. The first approach approximates the digital curve by minimizing the number of sides of the polygon and the approximation error should be less than a prespecified tolerance value. The second approach minimizes the approximation error by searching for a polygon with a given number of sides. The third approach, which is more practical, determines the approximating polygon automatically without any given condition. Moreover, a learning strategy for each of the proposed genetic algorithm is presented to improve the results. The experimental results show that the proposed approaches have better performances than those of existing methods.

Towards the Harmonious Mating for Genetic Algorithms

2011 ◽  
Vol 255-260 ◽  
pp. 2013-2017
Author(s):  
Fa Liang Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Experimental Results ◽  
Human Reproduction ◽  
Competitive Performance ◽  
Exploration And Exploitation

Genetic algorithms (GAs) have achieved lots of success in various applications, but the problem to balance exploration and exploitation of population is still up in the air. In this paper, we propose a variant of genetic algorithm with mating operator GASF to alleviate the problem; GASF measures the mating attractiveness of individuals from four aspects: gender, age, similarity and fitness. Individuals are assigned gender to facilitate mimicking human reproduction, and contributions of age, similarity and fitness to the attractiveness are respectively quantified and self-adaptively adjusted. Experimental results indicate that the proposed approach can achieve competitive performance with improved convergence.

QoS Routing Optimization Based on Improved Genetic Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 1169-1173
Author(s):  
Chang Lin He ◽  
Yu Fen Li ◽  
Lei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computational Complexity ◽  
Fitness Function ◽  
Qos Routing ◽  
Experimental Results ◽  
Improved Genetic Algorithm ◽  
Coding Schemes ◽  
Routing Optimization

A improved genetic algorithm is proposed to QoS routing optimization. By improving coding schemes, fitness function designs, selection schemes, crossover schemes and variations, the proposed method can effectively reduce computational complexity and improve coding accuracy. Simulations are carried out to compare our algorithm with the traditional genetic algorithms. Experimental results show that our algorithm converges quickly and is reliable. Hence, our method vastly outperforms the traditional algorithms.

Study of Photovoltaic Systems’ Performances with Different Module Types

2016 ◽  
Vol 856 ◽  
pp. 279-284 ◽  
Author(s):  
Zahari Zarkov ◽  
Ludmil Stoyanov ◽  
Hristiyan Kanchev ◽  
Valentin Milenov ◽  
Vladimir Lazarov
Keyword(s):  
Theoretical Study ◽  
Weather Conditions ◽  
Operating Conditions ◽  
Experimental Results ◽  
Photovoltaic Systems ◽  
Experimental Site ◽  
Pv Systems ◽  
Research Platform ◽  
Pv Modules ◽  
Different Types

The purpose of the work is to study and compare the performance of photovoltaic (PV) generators built with different types of panels and operating in real weather conditions. The paper reports the results from an experimental and theoretical study of systems with PV modules manufactured according to different technologies and using different materials. The experiment was carried out at a research platform for PV systems developed by the authors, built and located at an experimental site near the Technical University of Sofia. Based on the obtained results, comparisons are made between the different PV generators for the same operating conditions. The comparison between the theoretical and the experimental results demonstrates a good level of overlap.

Improvement and Application of Genetic Algorithms in Reactive Power Optimization of Power Network

2011 ◽  
Vol 354-355 ◽  
pp. 1058-1063
Author(s):  
Lin Lei ◽  
Yi Nan Ge ◽  
Qin Yuan
Keyword(s):  
Genetic Algorithms ◽  
High Voltage ◽  
Reactive Power ◽  
Power Optimization ◽  
Convergence Speed ◽  
Power Network ◽  
Reactive Power Optimization ◽  
Simple Genetic Algorithms ◽  
High Voltage System ◽  
Bus System

Reactive power optimization that is optimized by Simple Genetic Algorithms has many limitations. According to the problem of reactive power optimization of high voltage system, the Simple Genetic Algorithms is improved. The improved algorithm is applied in reactive power optimization of IEEE-6 bus system, the results indicate that the improvement is effective and it can accelerate the convergence speed and enhance the ability of optimization.

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