scholarly journals Topological Method for Assessing the Sensitivity to the Detection of Cybersecurity in Electrical Networks

2021 ◽  
Vol 43 (2) ◽  
pp. 68-78
Author(s):  
V.O. Gurieiev ◽  
◽  
Y.M. Lysenko ◽  
Keyword(s):  
Topological Method ◽  
Electrical Networks

Розглянуто теоретичні питання побудови топологічного методу оцінки чутливості до виявлення кібернетичних загроз в електричних мережах енергосистем за допомогою моделювання режимів роботи окремих (виділених) підсистем. Описано основні етапи побудови моделей топології енергосистем, запропоновано та реалізовано методи форму­вання інформаційних моделей об'єктів енергосистем. Досліджено методи візуалізації ре­зультатів моделювання умов виникнення кіберзагроз. Визначено способи використання запропонованого підходу до створення системи протидії кіберзагрозам в електричних мережах енергосистем і побудови сценаріїв їх ліквідації за допомогою навчального дис­танційного режимного тренажеру.

Study of the operation of an artificial neural network that works in the electric network in order to prevent emergency conditions.

2020 ◽  
Vol 14 (1) ◽  
pp. 48-54
Author(s):  
D. Ostrenko ◽  
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Energy System ◽  
Electrical Networks ◽  
Electric Networks ◽  
Electric Network ◽  
Time Dynamics ◽  
Emergency Situations

Emergency modes in electrical networks, arising for various reasons, lead to a break in the transmission of electrical energy on the way from the generating facility to the consumer. In most cases, such time breaks are unacceptable (the degree depends on the class of the consumer). Therefore, an effective solution is to both deal with the consequences, use emergency input of the reserve, and prevent these emergency situations by predicting events in the electric network. After analyzing the source [1], it was concluded that there are several methods for performing the forecast of emergency situations in electric networks. It can be: technical analysis, operational data processing (or online analytical processing), nonlinear regression methods. However, it is neural networks that have received the greatest application for solving these tasks. In this paper, we analyze existing neural networks used to predict processes in electrical systems, analyze the learning algorithm, and propose a new method for using neural networks to predict in electrical networks. Prognostication in electrical engineering plays a key role in shaping the balance of electricity in the grid, influencing the choice of mode parameters and estimated electrical loads. The balance of generation of electricity is the basis of technological stability of the energy system, its violation affects the quality of electricity (there are frequency and voltage jumps in the network), which reduces the efficiency of the equipment. Also, the correct forecast allows to ensure the optimal load distribution between the objects of the grid. According to the experience of [2], different methods are usually used for forecasting electricity consumption and building customer profiles, usually based on the analysis of the time dynamics of electricity consumption and its factors, the identification of statistical relationships between features and the construction of models.

Adsorption of carbon monoxide on the iridium fcc (112) surface: Topological study

1989 ◽  
Vol 54 (3) ◽  
pp. 566-571 ◽  
Author(s):  
Jiří Pancíř ◽  
Ivana Haslingerová
Keyword(s):  
Carbon Monoxide ◽  
Topological Method ◽  
Dissociative Chemisorption ◽  
Semi Empirical

A semi-empirical topological method was applied to a study of an Ir(112) surface as well as to both a nondissociative and dissociative chemisorption on this surface. In all cases studied an attachment of carbon to the surface is energetically more favorable than an attachment of oxygen. The preferential capture of the CO molecule on atop sites is remarkable. The capture on n-fold hollow positions as well as the dissociative chemisorption of carbon monoxide on the Ir(112) surface are energetically prohibited.

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