Online monitoring algorithm of power transformer based on outlier detection

2014 ◽  
Author(s):  
Jiwei Guo
Keyword(s):  
Outlier Detection ◽  
Online Monitoring ◽  
Power Transformer ◽  
Monitoring Algorithm

The Research of Spectral-Line Monitoring of Transformer Vibration

2011 ◽  
Vol 128-129 ◽  
pp. 1489-1492
Author(s):  
Qiao Lin Ding ◽  
Jian Chen ◽  
Li Qing Liu
Keyword(s):  
Fault Diagnosis ◽  
Spectral Line ◽  
Network Model ◽  
Online Monitoring ◽  
Vibration Spectrum ◽  
Power Transformer ◽  
Monitoring Methods ◽  
Extraction Scheme ◽  
Transformer Fault

This paper presents the using vibration spectrum of transformer operational status with online monitoring methods, analyzes and discusses the transformer vibration spectrum about the producing mechanism and extraction scheme . At the same time, it also discusses the feasibility and necessity of using vibration spectrum methods to monitor the transformer fault .Some vibration spectrum methods ,relating to the transformer research contents and key technical breakthrough direction, are summarized in detail. Finally, it put forward a new immune from organization antibody network model and antibody generating algorithm that is used for vibration spectrum power transformer fault diagnosis.

Evaluation of μFSBR joint performance by process-physics based quality criteria and online monitoring algorithm

2020 ◽  
Vol 278 ◽  
pp. 116508
Author(s):  
Haris Ali Khan ◽  
Shenli Pei ◽  
Nannan Chen ◽  
Scott Miller ◽  
Jingjing Li
Keyword(s):  
Online Monitoring ◽  
Quality Criteria ◽  
Joint Performance ◽  
Monitoring Algorithm

Research on Intelligent Online Monitoring and Evaluation of Power Transformer

2015 ◽  
Vol 9 (1) ◽  
pp. 483-489 ◽  
Author(s):  
Liu Tong
Keyword(s):  
Power System ◽  
Online Monitoring ◽  
Power Transformer ◽  
Full Range ◽  
Monitoring And Evaluation ◽  
Pressure Control ◽  
Safe Operation ◽  
Intelligent Monitoring ◽  
State Evaluation ◽  
On Line

Transformer is the power equipment of power system core. Intelligent monitoring and operation of transformer can not only monitor the full range of the transformer, the realization of state evaluation, it should be required repair, maintenance required good, but also on the latent fault early diagnosis and prediction. Research on transformer intelligent monitoring and operating technology of safe operation of power system and the substation has great significant and impoertance. According to the theory and method of transformer intelligent, combined with intelligent transformer on-line monitoring technology has been achieved, mainly explores the intelligent cooling and adjustable transformer pressure control technology.

Synthesis of Monitoring Rules with STL

2020 ◽  
Vol 29 (11) ◽  
pp. 2050177
Author(s):  
Sertac Kagan Aydin ◽  
Ebru Aydin Gol
Keyword(s):  
Genetic Algorithms ◽  
Web Services ◽  
Real Time ◽  
Temporal Logic ◽  
Online Monitoring ◽  
Cyber Physical Systems ◽  
Time Signal ◽  
Piecewise Constant ◽  
Physical Systems ◽  
Monitoring Algorithm

Online monitoring is essential to enhance the reliability for various systems including cyber-physical systems and Web services. During online monitoring, the system traces are checked against monitoring rules in real time to detect deviations from normal behaviors. In general, the rules are defined as boundary conditions by the experts of the monitored system. This work studies the problem of synthesizing online monitoring rules in the form of temporal logic formulas in an automated way. The monitoring rules are described as past-time signal temporal logic (ptSTL) formulas and an algorithm to synthesize such formulas from a given set of labeled system traces is proposed. The algorithm searches the formula space using genetic algorithms and produces the best formula representing a monitoring rule. In addition, online STL monitoring algorithm is improved to efficiently compute a quantitative valuation for piecewise-constant signals from ptSTL formulas, thus, to reduce the overhead of the real-time computation. The effectiveness of the results is shown on two illustrative examples inspired from online monitoring of Web services.

A Knowledge-Based Analytics System via Online Monitoring of Dissolved Gas in Power Transformer – A Conceptual Framework

2013 ◽  
Vol 845 ◽  
pp. 554-558
Author(s):  
Mohd Radzian A. Rahman ◽  
Mohd Iqbal Ridwan
Keyword(s):  
Conceptual Framework ◽  
Situational Awareness ◽  
Online Monitoring ◽  
Power Transformer ◽  
Engineering System ◽  
Dissolved Gas ◽  
Research Project ◽  
Knowledge Based ◽  
Monitoring Activities ◽  
Combustible Gases

The monitoring of dissolved combustible gases in power transformer oil could enable early detection of disastrous fault. The conventional dissolved gases in oil monitoring activities have these characteristic: 1) periodically sampling and 2) manual interpretation of combustible gases. However, periodical sampling increases number of undetected fault due to long sampling interval and manual interpretation of dissolved gas is often too complex for system operator to digest, resulting in reduced reliability of the power system and lack of situational awareness. To enhance the condition based monitoring activities for power transformer; TNB Research is embarking on online monitoring and knowledge-based system research project to address both issues related to periodical sampling method. This paper outlines the conceptual framework of the research project which was recently approved. It includes (1) the system architecture of the online monitoring system, (2) brief explanation of the mechanism of photo-acoustic spectroscopy, (3) the engineering system situational awareness framework which integrates different levels of automation and (4) blocks of knowledge sources theory used in modeling the engineering system.

scholarly journals Pd-Doped SnO2-Based Sensor Detecting Characteristic Fault Hydrocarbon Gases in Transformer Oil

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Weigen Chen ◽  
Qu Zhou ◽  
Tuoyu Gao ◽  
Xiaoping Su ◽  
Fu Wan
Keyword(s):  
Molecular Orbital ◽  
Online Monitoring ◽  
Gas Sensing ◽  
Power Transformer ◽  
Transformer Oil ◽  
Orbital Energy ◽  
Hydrocarbon Gases ◽  
X Ray ◽  
Molecular Orbital Energy ◽  
Highest Occupied Molecular Orbital

Methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2C2) are important fault characteristic hydrocarbon gases dissolved in power transformer oil. Online monitoring these gaseous components and their generation rates can present the operational state of power transformer timely and effectively. Gas sensing technology is the most sticky and tricky point in online monitoring system. In this paper, pure and Pd-doped SnO2nanoparticles were synthesized by hydrothermal method and characterized by X-ray powder diffraction, field-emission scanning electron microscopy, and energy dispersive X-ray spectroscopy, respectively. The gas sensors were fabricated by side-heated preparation, and their gas sensing properties against CH4, C2H6, C2H4, and C2H2were measured. Pd doping increases the electric conductance of the prepared SnO2sensors and improves their gas sensing performances to hydrocarbon gases. In addition based on the frontier molecular orbital theory, the highest occupied molecular orbital energy and the lowest unoccupied molecular orbital energy were calculated. Calculation results demonstrate that C2H4has the highest occupied molecular orbital energy among CH4, C2H6, C2H4, and C2H2, which promotes charge transfer in gas sensing process, and SnO2surfaces capture a relatively larger amount of electric charge from adsorbed C2H4.

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