Power equipment technical state assessment based on structural reliability characteristics

2016 ◽  
Author(s):  
A. I. Khalyasmaa ◽  
V. Ya. Sandakov ◽  
A. S. Semerikov
Keyword(s):  
Structural Reliability ◽  
State Assessment ◽  
Technical State ◽  
Power Equipment ◽  
Reliability Characteristics

Power Equipment Technical State Assessment Principles

2014 ◽  
Vol 492 ◽  
pp. 531-535 ◽  
Author(s):  
Stepan A. Dmitriev ◽  
Alexandra I. Khalyasmaa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Knowledge Base ◽  
Hybrid Model ◽  
State Assessment ◽  
Technical State ◽  
Model Construction ◽  
Power Equipment ◽  
Membership Functions

This article is devoted to the principles of power equipment technical state assessment at 35-220 kV substations. The article deals with the network hybrid model construction using methods of fuzzy logic and artificial neural network. Finally, in order to construct the knowledge base, a methodology of the power equipment technical state assessment, based on the membership functions, is introduced.

A Model of Weight Absolute Grey Correlation Degree for the Transformer Bushing State Assessment

2013 ◽  
Vol 345 ◽  
pp. 507-510
Author(s):  
Rong Gu ◽  
Jin Sha Yuan ◽  
Fei Lv
Keyword(s):  
Correlation Coefficients ◽  
State Assessment ◽  
The State ◽  
Condition Based Maintenance ◽  
Normal Operation ◽  
Power Equipment ◽  
Accurate Assessment ◽  
Grey Correlation ◽  
Correlation Degree ◽  
Grey Correlation Degree

Accurate assessment for the operational status of the transformer bushing is not only a prerequisite for the implementation of condition-based maintenance, but also to ensure the normal operation of the transformer and the whole power equipment conditions. In the paper, a model of weight absolute grey correlation degree was used to assess the transformer bushing state. Firstly, determined the assessment indicators and handled them. Secondly, the weights, correlation coefficients and each association were calculated. Thirdly, to compare the largest association of assessment indicators in grades as the transformer bushing final run state. Finally, give the state assessment results, according to the remark set. The result shows the model works well.

scholarly journals Machine learning as a tool of high-voltage electrical equipment lifecycle control enhancement

2020 ◽  
Vol 24 (5) ◽  
pp. 1093-1104
Author(s):  
Alexandra Khalyasmaa ◽  
Keyword(s):  
Machine Learning ◽  
Power System ◽  
High Voltage ◽  
Operation Mode ◽  
Electrical Equipment ◽  
Technical State ◽  
Power Equipment ◽  
System Operation ◽  
Power System Operation ◽  
Equipment State

The purpose of the study is to analyze the practical implementation of high-voltage electrical equipment technical state estimation subsystems as a part of solving the lifecycle management problem based on machine learning methods and taking into account the effect of the adjacent power system operation modes. To deal with the problem of power equipment technical state analysis, i.e. power equipment state pattern recognition, XGBoost based on gradient boosting decision tree algorithm is used. Its main advantages are the ability to process gapped data and efficient operation with tabular data for solving classification and regression problems. The author suggests the formation procedure of correct and sufficient initial database for high-voltage equipment state pattern recognition based on its technical diagnostic data and the algorithm for training and testing sets creation in order to improve the identification accuracy of power equipment actual state. The description and justification of the machine learning method and corresponding error metrics are also provided. Based on the actual states of power transformers and circuit breakers the sets of technical diagnostic parameters that have the greatest impact on the accuracy of state identification are formed. The effectiveness of using power systems operation parameters as additional features is also confirmed. It is determined that the consideration of operation parameters obtained by calculation as a part of the training set for high-voltage equipment technical state identification makes it possible to improve the tuning accuracy. The developed structure and approaches to power equipment technical state analysis supplemented by power system operation mode data and diagnostic results provide an information link between the tasks of technological and dispatch control. This allows us to consider the task of power system operation mode planning from the standpoint of power equipment technical state and identify the priorities in repair and maintenance to eliminate power network “bottlenecks”.

scholarly journals Technical State Assessment of Charge Exchange System of Self-Ignition Engine, Based On the Exhaust Gas Composition Testing

2017 ◽  
Vol 24 (s1) ◽  
pp. 203-212 ◽  
Author(s):  
Jacek Rudnicki ◽  
Ryszard Zadrąg
Keyword(s):  
Charge Exchange ◽  
Measurement Data ◽  
Multiple Regression Model ◽  
State Assessment ◽  
Technical State ◽  
Diagnostic Tools ◽  
Exhaust Gas ◽  
Gas Composition ◽  
Exchange System ◽  
Analytical Tools

Abstract This paper presents possible use of results of exhaust gas composition testing of self - ignition engine for technical state assessment of its charge exchange system under assumption that there is strong correlation between considered structure parameters and output signals in the form of concentration of toxic compounds (ZT) as well as unambiguous character of their changes. Concentration of the analyzed ZT may be hence considered to be symptoms of engine technical state. At given values of the signals and their estimates it is also possible to determine values of residues which may indicate a type of failure. Available tool programs aimed at analysis of experimental data commonly make use of multiple regression model which allows to investigate effects and interaction between model input quantities and one output variable. Application of multi-equation models provides great freedom during analysis of measurement data as it makes it possible to simultaneously analyze effects and interaction of many output variables. It may be also implemented as a tool for preparation of experimental material for other advanced diagnostic tools such as neural networks which, in contrast to multi-equation models, make it possible to recognize a state at multistate classification and - in consequence - to do diagnostic inference. Here , these authors present merits of application of the above mentioned analytical tools on the example of tests conducted on an experimental engine test stand.

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