Key Technology and Application Prospect of Operation and Maintenance of Power Equipment in New Power System

2021 ◽  
Keyword(s):  
Power System ◽  
Power Equipment ◽  
Key Technology ◽  
Operation And Maintenance

Possibility of Four-Phase Grids Application in Russian Power System

2015 ◽  
Vol 799-800 ◽  
pp. 1206-1210
Author(s):  
Dmitry I. Bliznyuk ◽  
Alexandra I. Khalyasmaa
Keyword(s):  
Economic Analysis ◽  
Power System ◽  
Simulation Model ◽  
Russian Federation ◽  
High Voltage ◽  
Power Grid ◽  
Operation And Maintenance ◽  
Electrical Grids ◽  
United Power System ◽  
Extra High Voltage

This paper is devoted to four-phase electrical grids of high and extra-high voltage, principles of its formation and its possible operation and maintenance in united power system of Russia. Development of new simulation model of four-phase power grid for further realization in software is under consideration. Special attention is given to compliance with present both technical and economical requirements of this model in Russian Federation. Economic analysis of four-phase power system applying is done by means of comparison of different kinds of transmission Ural – Siberia.

The Review of of Active Power Filter

2014 ◽  
Vol 940 ◽  
pp. 339-342 ◽  
Author(s):  
Xue Song Zhou ◽  
Wei Liu ◽  
You Jie Ma
Keyword(s):  
Power System ◽  
Electric Power ◽  
Active Power Filter ◽  
Active Power ◽  
Key Technology ◽  
Passive Filter ◽  
Development History ◽  
Power Filter ◽  
Quality Of Electric Power

Harmonics of power system seriously affected the quality of electric power. Active Power Filter (APF) [ which overcomes the shortcomings of passive filter (PF) has become main technology to suppress harmonic. In this paper, development history, key technology, problems and trends of APF are mainly analyzed and reviewed. With development of key technology of APF, it will have a better application prospect.

The Study of Switching Overvoltages under Power System Restoration Scenario Using Extended Delta-Bar-Delta Algorithm

2013 ◽  
Vol 14 (3) ◽  
pp. 219-230 ◽  
Author(s):  
Iman Sadeghkhani ◽  
Abbas Ketabi ◽  
Rene Feuillet
Keyword(s):  
Power System ◽  
New England ◽  
Random Search ◽  
Test System ◽  
Power Equipment ◽  
Case Scenario ◽  
Worst Case ◽  
Power System Restoration ◽  
System Restoration ◽  
Artificial Neural Network Ann

Abstract This paper presents an intelligent approach to evaluate switching overvoltages during power equipment energization. Switching action is one of the most important issues in power system restoration schemes. This action may lead to overvoltages that can damage some equipment and delay ‎power system restoration. In this work, transient overvoltages caused by power equipment energization are analyzed and estimated using artificial neural network (ANN)-based approach. Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD), and directed random search (DRS), were used to train the ANNs. In the cases of transformer and shunt reactor energization, ANNs are trained with the worst case scenario of switching angle and remanent flux which reduce the number of required simulations for training ANN. Also, for achieving good generalization capability for developed ANN, equivalent parameters of the network are used as ANN inputs. The simulated results for a partial of 39-bus New England test system, ‎show that the proposed technique can estimate the peak values and ‎duration of switching overvoltages with good accuracy and EDBD algorithm presents best performance.

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 OPTIMALISASI KAPASITAS ENERGI ANGIN DAN MATAHARI DENGAN KONFIGURASI MIKROGRID BERDASARKAN KARAKTERISTIK BEBAN

2021 ◽  
Vol 10 (2) ◽  
pp. 170
Author(s):  
Dhanis Woro Fittrin Selo Nur Giyatno ◽  
Lukman Bagus Subekti ◽  
Adlan Bagus Pradana ◽  
Indriana Nurmawati ◽  
Indra Wibowo
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
Vocational Education ◽  
Wind Turbine ◽  
Fossil Fuels ◽  
Electrical Energy ◽  
Job Skills ◽  
Technology Study ◽  
Study Program ◽  
Operation And Maintenance

Diploma III Electrical Technology Study Program is a institutional vocational education institutional in Department of Electrical Engineering and Informatics that aims to produce graduate who are ready to work in operation and maintenance of power system. Since phenomena of scarcity of fossil fuels, study program meet the 2 major problems, namely the limitations of electrical energy for practical lab work and increased job skills on the electrical energy conversion of electrical energy from renewable energy. The purpose of this research is to optimalize capacity of solar and wind energy contained in the environment of the laboratory on the microgrid configuration, namely PV-Wind turbine-Battery. Software HOMER is used to simulate microgrid configuration with AC-DC load, AC load, and DC load. The results show indicate that microgrid PV-Wind turbine-Battery is more economically to meet the need AC-DC load than the others. 

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