scholarly journals Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6351
Author(s):  
Łukasz Rokicki
Keyword(s):  
Immune System ◽  
Power Systems ◽  
Evolutionary Algorithm ◽  
Distribution System ◽  
Artificial Immune System ◽  
Low Voltage ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Clonal Selection Algorithm ◽  
Artificial Immune

The issue of optimization of the configuration and operating states in low voltage microgrids is important both from the point of view of the proper operation of the microgrid and its impact on the medium voltage distribution network to which such microgrid is connected. Suboptimal microgrid configuration may cause problems in networks managed by distribution system operators, as well as for electricity consumers and owners of microsources and energy storage systems connected to the microgrid. Structures particularly sensitive to incorrect determination of the operating states of individual devices are hybrid microgrids that combine an alternating current and direct current networks with the use of a bidirectional power electronic converter. An analysis of available literature shows that evolutionary and swarm optimization algorithms are the most frequently chosen for the optimization of power systems. The research presented in this article concerns the assessment of the possibilities of using artificial immune systems, operating on the basis of the CLONALG algorithm, as tools enabling the effective optimization of low voltage hybrid microgrids. In his research, the author developed a model of a hybrid low voltage microgrid, formulated three optimization tasks, and implemented an algorithm for solving the formulated tasks based on an artificial immune system using the CLONALG algorithm. The conducted research consisted of performing a 24 h simulation of microgrid operation for each of the formulated optimization tasks (divided into 10 min independent optimization periods). A novelty in the conducted research was the modification of the hypermutation operator, which is the key mechanism for the functioning of the CLONALG algorithm. In order to verify the changes introduced in the CLONALG algorithm and to assess the effectiveness of the artificial immune system in solving optimization tasks, optimization was also carried out with the use of an evolutionary algorithm, commonly used in solving such tasks. Based on the analysis of the obtained results of optimization calculations, it can be concluded that the artificial immune system proposed in this article, operating on the basis of the CLONALG algorithm with a modified hypermutation operator, in most of the analyzed cases obtained better results than the evolutionary algorithm. In several cases, both algorithms obtained identical results, which also proves that the CLONALG algorithm can be considered as an effective tool for optimizing modern power structures, such as low voltage microgrids, including hybrid AC/DC microgrids.

IMMUNOLOGY AND ARTIFICIAL IMMUNE SYSTEMS

2012 ◽  
Vol 21 (06) ◽  
pp. 1250031 ◽  
Author(s):  
MUHAMMAD ROZI MALIM ◽  
FARIDAH ABDUL HALIM
Keyword(s):  
Immune System ◽  
Network Theory ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Selection Mechanism ◽  
Artificial Immune ◽  
Time Line ◽  
Immune Systems

Artificial immune system is inspired by the natural immune system for solving computational problems. The immunological principles that are primarily used in artificial immune systems are the clonal selection principle, the immune network theory, and the negative selection mechanism. These principles have been applied in anomaly detection, pattern recognition, computer and network security, dynamic environments and learning, robotics, data analysis, optimization, scheduling, and timetabling. This paper describes how these three immunological principles were adapted by previous researchers in their artificial immune system models and algorithms. Finally, the applications of various artificial immune systems to various domains are summarized as a time-line.

scholarly journals Optimal Siting of Distributed Generators in a Distribution Network using Artificial Immune System

2017 ◽  
Vol 7 (2) ◽  
pp. 641 ◽  
Author(s):  
Meera P.S. ◽  
S. Hemamalini
Keyword(s):  
Immune System ◽  
Distribution System ◽  
Optimal Power Flow ◽  
Artificial Immune System ◽  
Voltage Stability ◽  
Clonal Selection ◽  
Distribution Network ◽  
Distribution Networks ◽  
Optimal Location ◽  
Artificial Immune

<p>Distributed generation (DG) sources are being installed in distribution networks worldwide due to their numerous advantages over the conventional sources which include operational and economical benefits. Random placement of DG sources in a distribution network will result in adverse effects such as increased power loss, loss of voltage stability and reliability, increase in operational costs, power quality issues etc. This paper presents a methodology to obtain the optimal location for the placement of multiple DG sources in a distribution network from a technical perspective. Optimal location is obtained by evaluating a global multi-objective technical index (MOTI) using a weighted sum method. Clonal selection based artificial immune system (AIS) is used along with optimal power flow (OPF) technique to obtain the solution. The proposed method is executed on a standard IEEE-33 bus radial distribution system. The results justify the choice of AIS and the use of MOTI in optimal siting of DG sources which improves the distribution system efficiency to a great extent in terms of reduced real and reactive power losses, improved voltage profile and voltage stability. Solutions obtained using AIS are compared with Genetic algorithm (GA) and Particle Swarm optimization (PSO) solutions for the same objective function.</p>

Developing an Algorithm for the Application of Bayesian Method to Software Using Artificial Immune Systems

2021 ◽  
Author(s):  
Shafagat Mahmudova
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Bayesian Method ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Computational System ◽  
Malicious Software ◽  
Immune Systems ◽  
Advantages And Disadvantages ◽  
Protection Systems

Abstract This study provides information on artificial immune systems. The artificial immune system is an adaptive computational system that uses models, principles, mechanisms and functions to describe and solve the problems in theoretical immunology. Its application in various fields of science is explored. The theory of natural immune systems and the key features and algorithms of artificial immune system are analyzed. The advantages and disadvantages of protection systems based on artificial immune systems are shown. The methods for malicious software detection are studied. Some works in the field of artificial immune systems are analyzed, and the problems to be solved are identified. A new algorithm is developed for the application of Bayesian method in software using artificial immune systems, and experiments are implemented. The results of the experiment are estimated to be good. The advantages and disadvantages of AIS were shown. To eliminate the disadvantages, perfect AISs should be developed to enable the software more efficient and effective.

Performance of Negative Selection Algorithms in Patient Detection and Classification

2018 ◽  
pp. 78-102
Author(s):  
Orhan Bölükbaş ◽  
Harun Uğuz
Keyword(s):  
Immune System ◽  
Error Detection ◽  
Classification Accuracy ◽  
Negative Selection ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Detection Accuracy ◽  
Data Sets ◽  
Selection Algorithm ◽  
Artificial Immune

Artificial immune systems inspired by the natural immune system are used in problems such as classification, optimization, anomaly detection, and error detection. In these problems, clonal selection algorithm, artificial immune network algorithm, and negative selection algorithm are generally used. This chapter aims to solve the problem of correct identification and classification of patients using negative selection (NS) and variable detector negative selection (V-DET NS) algorithms. The authors examine the performance of NSA and V-DET NSA algorithms using three sets of medical data sets from Parkinson, carotid artery doppler, and epilepsy patients. According to the obtained results, NSA achieved 92.45%, 91.46%, and 92.21% detection accuracy and 92.46%, 93.40%, and 90.57% classification accuracy. V-DET NSA achieved 94.34%, 94.52%, and 91.51% classification accuracy and 94.23%, 94.40%, and 89.29% detection accuracy. As can be seen from these values, V-Det NSA yielded a better result. Artificial immune system emerges as an effective and promising system in terms of problem-solving performance.

Extracting Information in Agricultural Data Using Fuzzy-Rough Sets Hybridization and Clonal Selection Theory Inspired Algorithms

2016 ◽  
Vol 30 (09) ◽  
pp. 1660008 ◽  
Author(s):  
Ayodele Lasisi ◽  
Rozaida Ghazali ◽  
Mustafa Mat Deris ◽  
Tutut Herawan ◽  
Fola Lasisi
Keyword(s):  
Feature Selection ◽  
Artificial Immune System ◽  
Clonal Selection ◽  
Recognition System ◽  
Clonal Selection Algorithm ◽  
Immune Recognition ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Detection Rates ◽  
Clonal Selection Theory

Mining agricultural data with artificial immune system (AIS) algorithms, particularly the clonal selection algorithm (CLONALG) and artificial immune recognition system (AIRS), form the bedrock of this paper. The fuzzy-rough feature selection (FRFS) and vaguely quantified rough set (VQRS) feature selection are coupled with CLONALG and AIRS for improved detection and computational efficiencies. Comparative simulations with sequential minimal optimization and multi-layer perceptron reveal that the CLONALG and AIRS produced significant results. Their respective FRFS and VQRS upgrades namely, FRFS-CLONALG, FRFS-AIRS, VQRS-CLONALG, and VQRS-AIRS, are able to generate the highest detection rates and lowest false alarm rates. Thus, gathering useful information with the AIS models can help to enhance productivity related to agriculture.

THE MODEL AND ALGORITHM ARTIFICIAL IMMUNE SYSTEM

2016 ◽  
Vol 8 (3) ◽  
pp. 5-10
Author(s):  
Астахова ◽  
I. Astakhova ◽  
Ушаков ◽  
S. Ushakov
Keyword(s):  
Neural Networks ◽  
Immune System ◽  
Artificial Immune System ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Program Complex ◽  
Immune Systems ◽  
The Creation

In particular, models had only one type of cages , they applied V-lymphocytes. The distribution and a decentralization were the second feature for using artificial immune systems. This article is devoted to creation the artificial immune system (AIS), the creation model and algorithm of IIS is considered. The model for realization of a problem is consid-ered. Accuracy of calculations is compared to other methods, especially to neural networks. The structure of a program complex is described.

How Do We Evaluate Artificial Immune Systems?

2005 ◽  
Vol 13 (2) ◽  
pp. 145-177 ◽  
Author(s):  
Simon M. Garrett
Keyword(s):  
Immune System ◽  
Negative Selection ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Immune Systems ◽  
Danger Theory

The field of Artificial Immune Systems (AIS) concerns the study and development of computationally interesting abstractions of the immune system. This survey tracks the development of AIS since its inception, and then attempts to make an assessment of its usefulness, defined in terms of ‘distinctiveness’ and ‘effectiveness.’ In this paper, the standard types of AIS are examined—Negative Selection, Clonal Selection and Immune Networks—as well as a new breed of AIS, based on the immunological ‘danger theory.’ The paper concludes that all types of AIS largely satisfy the criteria outlined for being useful, but only two types of AIS satisfy both criteria with any certainty.

Sign in / Sign up

Export Citation Format

Share Document