scholarly journals Management of Oil Transportation via Main Pipelines using SCADA System Data

2019 ◽  
Author(s):  
T Bekibayev ◽  
U Zhapbasbayev ◽  
G Ramazanova
Keyword(s):  
Scada System ◽  
Oil Transportation ◽  
Main Pipelines ◽  
System Data

State Estimation and SCADA System Data Cooperation Problem Analysis

2012 ◽  
Vol 616-618 ◽  
pp. 2187-2191
Author(s):  
Fang Liu ◽  
Hai Bao
Keyword(s):  
State Estimation ◽  
Sampling Methods ◽  
Measured Data ◽  
Dynamic State ◽  
Problem Analysis ◽  
Electrical Measurements ◽  
Scada System ◽  
State Measurement ◽  
System Data ◽  
Steady State Measurement

Steady-state measurement value is the calculation premise of state estimation. However, power grid operates more often under dynamic state in actual practice. Thus, applying measured data from SCADA in state estimation directly lead to the incorrect and inaccurate calculation result. Based on the premise of state estimation calculation, the online electrical measurements and sampling methods are analyzed, and the deviation between real electrical values and sampling data is calculated. The cooperation problem between SCADA data and state estimation is proved using MATLAB/Simulink Software.

Detection of oil losses in main pipeline damage

2021 ◽  
pp. 31-36
Author(s):  
F.R. Mehdiyev ◽  
◽  
◽  
Keyword(s):  
Analytical Method ◽  
Main Pipeline ◽  
Failure Prevention ◽  
Oil Pipeline ◽  
Oil Transportation ◽  
Main Pipelines ◽  
Efficient Control ◽  
Definition Of ◽  
Protection From Corrosion

The enterprises and companies dealing with the oil transportation and operation of oil pipeline pay special attention to the improvement of methods for spill detection and prevention of oil losses. The improvement of the methods and means of failure prevention, development of reliable ways of pipeline protection from corrosion, as well as the methods of well-timed specifiction of damage point and loss elimination are necessary for efficient control of oil losses in the failures. The paper analyzes the reasons for occurring failures in the main pipelines. The graphic-analytical method for definition of damage point within the pipeline is presented as well.

scholarly journals Deep Learning Method for Fault Detection of Wind Turbine Converter

2021 ◽  
Vol 11 (3) ◽  
pp. 1280 ◽  
Author(s):  
Cheng Xiao ◽  
Zuojun Liu ◽  
Tieling Zhang ◽  
Xu Zhang
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Wind Turbine ◽  
Network Models ◽  
Detection Accuracy ◽  
Neural Network Models ◽  
Scada System ◽  
Indicator Variables ◽  
System Data

The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper presents an approach to wind turbine converter fault detection using convolutional neural network models which are developed by using wind turbine Supervisory Control and Data Acquisition (SCADA) system data. The approach starts with the selection of fault indicator variables, and then the fault indicator variables data are extracted from a wind turbine SCADA system. Using the data, radar charts are generated, and the convolutional neural network models are applied to feature extraction from the radar charts and characteristic analysis of the feature for fault detection. Based on the analysis of the Octave Convolution (OctConv) network structure, an improved AOctConv (Attention Octave Convolution) structure is proposed in this paper, and it is applied to the ResNet50 backbone network (named as AOC–ResNet50). It is found that the algorithm based on AOC–ResNet50 overcomes the issues of information asymmetry caused by the asymmetry of the sampling method and the damage to the original features in the high and low frequency domains by the OctConv structure. Finally, the AOC–ResNet50 network is employed for fault detection of the wind turbine converter using 10 min SCADA system data. It is verified that the fault detection accuracy using the AOC–ResNet50 network is up to 98.0%, which is higher than the fault detection accuracy using the ResNet50 and Oct–ResNet50 networks. Therefore, the effectiveness of the AOC–ResNet50 network model in wind turbine converter fault detection is identified. The novelty of this paper lies in a novel AOC–ResNet50 network proposed and its effectiveness in wind turbine fault detection. This was verified through a comparative study on wind turbine power converter fault detection with other competitive convolutional neural network models for deep learning.

scholarly journals Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 201 ◽  
Author(s):  
Kevin Leahy ◽  
Colm Gallagher ◽  
Peter O’Donovan ◽  
Dominic T. J. O’Sullivan
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Maintenance Strategies ◽  
Scada System ◽  
System Data ◽  
The Common ◽  
Common Problems ◽  
Original Equipment Manufacturers ◽  
Historical Periods ◽  
Operational Data

In order to remain competitive, wind turbines must be reliable machines with efficient and effective maintenance strategies. However, thus far, wind turbine reliability information has been closely guarded by the original equipment manufacturers (OEMs), and turbine reliability studies often rely on data that are not always in a usable or consistent format. In addition, issues with turbine maintenance logs and alarm system data can make it hard to identify historical periods of faulty operation. This means that building new and effective data-driven condition monitoring techniques and methods can be challenging, especially those that rely on supervisory control and data acquisition (SCADA) system data. Such data are rarely standardised, resulting in challenges for researchers in contextualising these data. This work aims to summarise some of the issues seen in previous studies, highlighting the common problems seen by researchers working in the areas of condition monitoring and reliability analysis. Standards and policy initiatives that aim to alleviate some of these problems are given, and a summary of their recommendations is presented. The main finding from this work is that industry would benefit hugely from unified standards for turbine taxonomies, alarm codes, SCADA operational data and maintenance and fault reporting.

Russian Oil: Production and Exports

2003 ◽  
pp. 136-146
Author(s):  
K. Liuhto
Keyword(s):  
Baltic Sea ◽  
Statistical Data ◽  
Oil Production ◽  
The Baltic Sea ◽  
North West ◽  
The North ◽  
Oil Transportation ◽  
Sea Ports ◽  
The Baltic

Statistical data on reserves, production and exports of Russian oil are provided in the article. The author pays special attention to the expansion of opportunities of sea oil transportation by construction of new oil terminals in the North-West of the country and first of all the largest terminal in Murmansk. In his opinion, one of the main problems in this sphere is prevention of ecological accidents in the process of oil transportation through the Baltic sea ports.

SenCity – Evaluating Users’ Experiences of Intelligent Lighting for Well-Being in Smart Cities

2018 ◽  
pp. 60-67
Author(s):  
Henrika Pihlajaniemi ◽  
Anna Luusua ◽  
Eveliina Juntunen
Keyword(s):  
Case Studies ◽  
Smart City ◽  
Smart Cities ◽  
Well Being ◽  
Evaluation Methods ◽  
Data Analyses ◽  
Sense Of Security ◽  
System Data

This paper presents the evaluation of usersХ experiences in three intelligent lighting pilots in Finland. Two of the case studies are related to the use of intelligent lighting in different kinds of traffic areas, having emphasis on aspects of visibility, traffic and movement safety, and sense of security. The last case study presents a more complex view to the experience of intelligent lighting in smart city contexts. The evaluation methods, tailored to each pilot context, include questionnaires, an urban dashboard, in-situ interviews and observations, evaluation probes, and system data analyses. The applicability of the selected and tested methods is discussed reflecting the process and achieved results.

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