Recurrent Neural Network-based Base Transceiver Station Power Supply System Failure Prediction

2020 ◽  
Author(s):  
Yonas Yehualaeshet Tefera ◽  
Tewodros Kibatu ◽  
Bethelhem Seifu Shawel ◽  
Dereje Hailemariam Woldegebreal
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Power Supply ◽  
Power Supply System ◽  
Failure Prediction ◽  
Supply System ◽  
System Failure ◽  
Base Transceiver Station

Study on Energy Coordination of Building Power Supply System Based on Neural Network

2019 ◽  
Author(s):  
Hui Wang ◽  
Qi Yao ◽  
Xiangping Meng ◽  
Chunhui Liang ◽  
Yinping An ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Supply ◽  
Power Supply System ◽  
Supply System

Application of Markov Chain to Determine the Electric Energy Supply System Reliability for the Cargo Control System of LNG Carriers

2013 ◽  
Author(s):  
Erick Miguel Portugal Hidalgo ◽  
Dennis Wilfredo Roldán Silva ◽  
Gilberto Francisco Martha de Souza
Keyword(s):  
Markov Chain ◽  
Control System ◽  
Power Supply ◽  
Energy Supply ◽  
Power Supply System ◽  
Electric Energy ◽  
Supply System ◽  
System Failure ◽  
Energy Supply System ◽  
Electric Power Supply

The natural gas industry, as well as other industrial activities, is not free from accidents, which can cause serious consequences to the integrity of people and properties. For this reason, it is necessary to develop studies to determine what are the possible causes of LNG leakage during the loading or unloading operation. This paper aims to determine the reliability of the Cargo control system electric power supply system applying Markov Chain technique. This reliability modeling tool is used because the system consists of several components in passive parallel. Based on the reliability analysis maintenance recommendations are proposed aiming at reducing system failure probability. The method used in this paper is divided in three steps. The first step consists in carrying out a study to identify what are the components that are part of electric power supply system. The second step involves the reliability analysis of the electric energy supply system. Finally some maintenance recommendations are presented aiming at reducing system failure rate. This analysis is used for the analysis of a LNG carrier operating in a Brazilian harbor indicating the most probability failure modes of this system and the most suitable maintenance actions to avoid them.

Reliability Research of Power-Supply System in Nuclear Power Plant Based on Markov Theory and Bayes Network

2010 ◽  
Author(s):  
Li Zhen ◽  
Xinli Sun ◽  
Jian Ding ◽  
Xia Zhang ◽  
Xinghui Cai
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Power Supply ◽  
System Reliability ◽  
Nuclear Power ◽  
Posterior Probability ◽  
Power Supply System ◽  
Supply System ◽  
System Failure ◽  
Bayes Network

As components in an actual nuclear system have multi states and are dependent on each other, a method for system reliability analyse based on Markov theory and Bayes network is presented in this paper. The method resolves the problems of multi-state and dependence simultaneously, by utilizing Markov theory and Bayes network synthetically. Applying the method to analysing reliability of power-supply system in nuclear power plant, the system failure probability, RAW and posterior probability of each component are obtained, which are useful for system reliability evaluation, improvement of weakness and fault diagnosis. Synchronously, the method is proved feasible and effective for solving system reliability that contains dependent and multi-state components.

scholarly journals Methods for calculating the reliability of power supply systems

2021 ◽  
Vol 25 (1) ◽  
pp. 57-65
Author(s):  
A. S. Lukovenko ◽  
I. V. Zenkov
Keyword(s):  
Neural Network ◽  
Failure Rate ◽  
Power Supply ◽  
Software Package ◽  
Power Supply System ◽  
Supply System ◽  
Reliability Indicators ◽  
Artificial Neural ◽  
Supply Systems ◽  
Technical Reliability

The aim was to determine the reliability indicators of a power supply system using an artificial neural network model. A model for calculating technical reliability was developed using the following methods: an algorithm for calculating reliability indicators of power supply systems, the method of failure rate of a power supply system and a forecasting model using artificial neural networks. It was established that a power supply system is formed by an open radial power supply circuit. The failure rate of the power supply subsystem was determined by calculating the failure rate of i-th element of the subsystem. As a result of calculating the probability of failure-free operation of the subsystem for various conditions (5 time intervals), it was found that with an increase in the operating time from 100 to 500 h, a linear increase in the rate of system failures occurs from 0.0051 to 0.0073 1/h. A comparison of the obtained mean-to-failure values of the main and the same backup subsystem in the unloaded mode with an absolutely reliable switch (269.62 h) with the main and the same backup subsystem in the loaded mode (202.21 h) was carried out. The results differ by 67.41 h, which indicates a higher degree of reliability of the first method. The software package Prognoz_INS_2020 was developed. An acceptable accuracy of no more than 2.17% was obtained by comparing the results of the conventional calculation of the failure rate of power supply systems and using the Prognoz_INS_2020 software package. This indicates the efficiency of the proposed software package in reliability calculations at operating energy enterprises. The proposed methods for assessing technical reliability both using the conventional model and a model based on an artificial neural network made it possible to assess the state of power supply systems, which helps to prevent dangerous emergencies. 

Sign in / Sign up

Export Citation Format

Share Document