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1

A new method for estimating fault location in radial medium voltage distribution network only using measurements at feeder beginning

Ngoc-Hung Truong

Archives of Electrical Engineering

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2023

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Vol. 72, nr 2

503--520

EN

This paper proposes a new fault location method in radial medium voltage distribution networks. The proposed method only uses the measurement data at the feeder beginning to approximate the characteristic equation showing the dependence between the positive-sequence voltage and phase angle at the monitoring point with the distance to the fault location for each fault type on each line segment. To determine these characteristic equation coefficients, the entire distribution network will be modeled and simulated by four types of faults at different locations along the lines to build the initial database. Based on this database, the mathematical functions in MATLAB software are applied to approximate these coefficients corresponding to each type of fault for each line segment in the network. Then, from the current and voltage measurement data at the feeder beginning, the algorithms of global search, comparison, and fault ranking are used to find out where the fault occurs on the distribution network. Two types of distribution network with and without branches are studied and simulated in this paper to verify and evaluate the effectiveness of the proposed method.

2

Falcon optimization algorithm for bayesian network structure learning

Kareem Shahab Wahhab, Okur Mehmet Cudi

Computer Science

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2021

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T. 22 (4)

553--569

EN

In machine-learning, some of the helpful scientific models during the production of a structure of knowledge are Bayesian networks. They can draw the relationships of probabilistic dependency among many variables. The score and search method is a tool that is used as a strategy for learning the structure of a Bayesian network. The authors apply the falcon optimization algorithm (FOA) to the learning structure of a Bayesian network. This paper has employed reversing, deleting, moving, and inserting to obtain the FOA for approaching the optimal solution of a structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is associated with pigeon-inspired optimization, greedy search, and simulated annealing that apply the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques by utilizing several benchmark data sets. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values).

3

Applications of genetic algorithms in nanoscience : A short survey of recent results

Paszkowicz W.

Computer Methods in Materials Science

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2013

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Vol. 13, No. 1

127--134

EN

Global-search procedures are an important element of materials design, processing, and properties determination. Genetic algorithms, a subset of algorithms based on evolutionary computation methods, are an example of global optimization methods inspired by biological principles of evolution. In materials science and related fields of science and technology, these algorithms are successfully used, e.g., for optimization of material elaboration and for design of materials with desired physical or structural properties, in working out modern devices based on specific physical principles, as well as in elaboration of improved methods of materials characterization. Nanomaterials science is a rapidly growing subfield of materials science covering various objects characterized by nanometric size. In this review, examples of recent applications of genetic algorithms in nanomaterials science are presented. Representative examples illustrate how useful are such computational methods for solving the scientific tasks, e.g., for thin-film growth modeling and characterization, for optimization of quantum-dot systems and of nanoparticle based medical therapies, for design of hard nanocomposite materials and for optimization of nanomaterial-based optical nanodevices and sensors of various gases

PL

Procedury przeszukiwania globalnego są ważnym elementem projektowania materiałów, ich wytwarzania i określania właściwości. Algorytmy genetyczne, stanowiące podzbiór algorytmów opartych o obliczenia ewolucyjne, stanowią przykład metody optymalizacji globalnej inspirowanej przez biologiczne prawa ewolucji. W nauce o materiałach i w pokrewnych dziedzinach nauki i techniki, algorytmy genetyczne są z powodzeniem stosowane, na przykład, w celu optymalizacji wytwarzania, przy projektowaniu materiałów o pożądanych właściwościach fizycznych lub strukturalnych, w opracowaniu nowoczesnych urządzeń działających na podstawie określonych zasad fizycznych, jak również w udoskonalaniu metod charakteryzacji materiałów. Nauka o nanomateriałach jest szybko rozwijającą się dziedziną materiałoznawstwa obejmującą różnorodne obiekty charakteryzujące się rozmiarami w skali nano. W niniejszym przeglądzie zaprezentowano przykłady ostatnio opisanych zastosowań algorytmów genetycznych w nauce o nanomateriałach. Reprezentatywne przykłady ilustrują, jak przydatne są takie metody obliczeniowe w rozwiązywaniu zadań naukowych, np. dla celów modelowania wzrostu cienkich warstw i dla ich charakteryzacji, dla optymalizacji układów kropek kwantowych i terapii medycznych bazujących na nanocząstkach, dla projektowania materiałów nanokompozytowych o wysokiej twardości i dla optymalizacji nanourządzeń optycznych i czujników różnego rodzaju gazów.