scholarly journals System of Water Vehicle Power Plant Remote Condition Monitoring

2021 ◽  
Vol 1199 (1) ◽  
pp. 012049
Author(s):  
A Golovan ◽  
I Honcharuk ◽  
O Deli ◽  
O Kostenko ◽  
Y Nykyforov
Keyword(s):  
Power Plant ◽  
Condition Monitoring ◽  
Condition Based Maintenance ◽  
Quality Data ◽  
Maximum Efficiency ◽  
Auxiliary Equipment ◽  
Monitoring Model ◽  
Repair Costs ◽  
Oil Fuel ◽  
Maintenance Process

Abstract Remote condition monitoring of water vehicles plays an important role in preventing potentially very expensive marine incidents and ensuring maximum efficiency of a ship's operation and reliability with minimum maintenance downtime and repair costs. Concept of the condition-based approach to maintenance is today's best practise, and it is becoming increasingly important to move from planned maintenance to condition-based maintenance, to reduce the increasingly high cost of maintaining a modern fleet. Onboard and remote monitoring is now an essential part of condition-based maintenance process to obtain the good quality data, correct analysis, and effective counteractive actions necessary for such an approach, and article presents the water vehicle power plant monitoring model developed by authors. Considered approach, coupled with preventive maintenance, saves shipowners time and money through early diagnosis of component failure or excess wear. Power plant of water vehicle comprises far more than just an engine with its auxiliary equipment but also other main propulsion blocks – in particular, thrusters. The result was the development of the Water Vehicle Condition Monitoring (WVCM) system, which enables to closely examine water vehicle equipment performance. A WVCM system comprises the following installed onboard: accelerometers, pressure and temperature transmitters, oil, fuel and exhaust monitoring units and a torque measurement system.

scholarly journals Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 304
Author(s):  
Sakthivel Ganesan ◽  
Prince Winston David ◽  
Praveen Kumar Balachandran ◽  
Devakirubakaran Samithas
Keyword(s):  
Standard Deviation ◽  
Power Quality ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Classification Accuracy ◽  
Induction Motors ◽  
Quality Data ◽  
Starting Current ◽  
Quality Issues ◽  
Condition Monitoring System

Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, industries have power quality issues such as sag, swell, harmonics, and transients. Thus, a condition monitoring system should have the ability to detect various faults, even in the presence of power quality issues. Most of the fault diagnosis and condition monitoring methods proposed earlier misidentified the faults and caused the condition monitoring system to fail because of misclassification due to power quality. The proposed method uses power quality data along with starting current data to identify the broken rotor bar and bearing fault in induction motors. The discrete wavelet transform (DWT) is used to decompose the current waveform, and then different features such as mean, standard deviation, entropy, and norm are calculated. The neural network (NN) classifier is used for classifying the faults and for analyzing the classification accuracy for various cases. The classification accuracy is 96.7% while considering power quality issues, whereas in a typical case, it is 93.3%. The proposed methodology is suitable for hardware implementation, which merges mean, standard deviation, entropy, and norm with the consideration of power quality issues, and the trained NN proves stable in the detection of the rotor and bearing faults.

scholarly journals Combined Correlation and Cluster Analysis for Long-Term Power Quality Data from Virtual Power Plant

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 641
Author(s):  
Michał Jasiński
Keyword(s):  
Cluster Analysis ◽  
Power Plant ◽  
Power Quality ◽  
Quality Data ◽  
Virtual Power ◽  
Distributed Resources ◽  
Virtual Power Plant ◽  
And Cluster Analysis ◽  
Global Indicators

Analysis of the connection between different units that operate in the same area assures always interesting results. During this investigation, the concerned area was a virtual power plant (VPP) that operates in Poland. The main distributed resources included in the VPP are a 1.25 MW hydropower plant and an associated 0.5 MW energy storage system. The mentioned VPP was a source of synchronic, long-term, multipoint power quality (PQ) data. Then, for five related measurement points, the conclusion about the relation in point of PQ was performed using correlation analysis, the global index approach, and cluster analysis. Global indicators were applied in place of PQ parameters to reduce the amount of analyzed data and to check the correlation between phase values. For such a big dataset, the occurrence of outliers is certain, and outliers may affect the correlation results. Thus, to find and exclude them, cluster analysis (k-means algorithm, Chebyshev distance) was applied. Finally, the correlation between PQ global indicators of different measurement points was performed. It assured general information about VPP units’ relation in point of PQ. Under the investigation, both Pearson’s and Spearman’s rank correlation coefficients were considered.

scholarly journals Condition Monitoring of Wind Turbine Based on Copula Function and Autoregressive Neural Network

2018 ◽  
Vol 198 ◽  
pp. 04008
Author(s):  
Zhongshan Huang ◽  
Ling Tian ◽  
Dong Xiang ◽  
Sichao Liu ◽  
Yaozhong Wei
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Copula Function ◽  
Single Step ◽  
Fusion Model ◽  
Monitoring Method ◽  
Monitoring Model ◽  
Autoregressive Neural Network

The traditional wind turbine fault monitoring is often based on a single monitoring signal without considering the overall correlation between signals. A global condition monitoring method based on Copula function and autoregressive neural network is proposed for this problem. Firstly, the Copula function was used to construct the binary joint probability density function of the power and wind speed in the fault-free state of the wind turbine. The function was used as the data fusion model to output the fusion data, and a fault-free condition monitoring model based on the auto-regressive neural network in the faultless state was established. The monitoring model makes a single-step prediction of wind speed and power, and statistical analysis of the residual values of the prediction determines whether the value is abnormal, and then establishes a fault warning mechanism. The experimental results show that this method can provide early warning and effectively realize the monitoring of wind turbine condition.

Pressure Testing Feedwater Heaters and Power Plant Auxiliary Heat Exchangers

2010 ◽  
Author(s):  
Stanley Yokell
Keyword(s):  
Heat Transfer ◽  
Power Plant ◽  
Heat Exchangers ◽  
Wire Drawing ◽  
Pressure Vessels ◽  
Shell Side ◽  
Auxiliary Equipment ◽  
National Board ◽  
Pressure Testing ◽  
Test Pressure

This paper discusses factory and field pressure testing of tubular heat transfer equipment such as closed feedwater heaters, steam surface condensers and power plant auxiliary heat exchangers built to Section VIII Division 1 of the ASME Boiler and Pressure Vessel Code (the ASME Code) and repaired or altered in accordance with the National Board Inspection Code (NBIC). It discusses the ASME Code’s and the NBIC’s requirements for hydrostatically testing unfired pressure vessels which includes tubular heat transfer equipment. It points out that using pressure gage indications of pressure loss to determine if there is a leak from the tube side to the shell side when the back face of the tubesheet is not visible does not reveal very small leaks or weeping. For the purposes of this paper, we define weeping, VRRLeak, as a leak of 20 drops per hour or approximately 1 cm3 [0.061 in3]. During typical half-hour hydrostatic test pressure holding periods, such weeping would amount to 10 drops of water on the tubesheet face or 0.5 cm3 [0.0305 in3]. Weeping through tube-to-tubesheet joints of high-pressure feedwater heaters can lead to wire drawing (wormholing), which can materially reduce the heater life. Leaks from the channel to the shell side of steam surface condensers and auxiliary condensers can introduce brackish water into the condensate. Depending upon the fluid flowing in the tubes, contaminants can enter the shell side of other auxiliary equipment when the channel pressure is higher than that of the shell. The paper concludes that Users must advise Designers and Manufacturers of the hazards of small leaks through the tube-to-tubesheet joints. It recommends that these three entities must agree on suitable leak tests.

scholarly journals Condition-Monitoring System for Identification and Representation of the Capability Diagram Limits for Multiple Synchronous Generators in a Hydro Power-Plant

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3800
Author(s):  
Boris Glavan ◽  
Zlatko Hanić ◽  
Marinko Kovačić ◽  
Mario Vražić
Keyword(s):  
Power Plant ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Synchronous Generator ◽  
Computationally Efficient ◽  
Synchronous Generators ◽  
Hydro Power ◽  
Automation And Control ◽  
Condition Monitoring System ◽  
And Control

This paper presents an experience in the design and implementation of the condition-monitoring system for the synchronous generators whose primary purpose is to record data for the identification of the capability limits of the P–Q diagram of three generators in hydro power-plant. Paper presents details about the monitoring system, the underlying theory of the identification of the synchronous generator model with a focus on the calculation of the capability limits in the P–Q diagram. Furthermore, a computationally efficient method for the representation of capability limits suitable for the implementation within the industrial automation and control system of the power-plant is described in detail. Finally, the capability diagrams for three generators were implemented in the power-plant supervisory control and data acquisition system (SCADA) system.

scholarly journals An Open Source Framework Approach to Support Condition Monitoring and Maintenance

2020 ◽  
Vol 10 (18) ◽  
pp. 6360
Author(s):  
Jaime Campos ◽  
Pankaj Sharma ◽  
Michele Albano ◽  
Luis Lino Ferreira ◽  
Martin Larrañaga
Keyword(s):  
Condition Monitoring ◽  
Open System ◽  
Health Monitoring ◽  
Large Scale ◽  
Condition Based Maintenance ◽  
Monitoring Methods ◽  
Industry Standards ◽  
Framework Approach ◽  
Push Forward ◽  
Predictive Health

This paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and analysis by simulating faults. The authors first undertook the leading industry standards for condition-based maintenance (CBM), i.e., open system architecture–condition-based maintenance (OSA–CBM) and Machinery Information Management Open System Alliance (MIMOSA), which has been working towards standardizing the integration and interchangeability between systems. In addition, this paper highlights the predictive health monitoring methods that are needed for an effective CBM approach. The monitoring of industrial machines is discussed as well as the necessary details are provided regarding a demonstrator built on a metal sheet bending machine of the Greenbender family. Lastly, the authors discuss the benefits of the integration of the developed prototypes into a service-oriented platform, namely the Arrowhead Framework, which can be instrumental for the remotization of maintenance activities, such as the analysis of various equipment that are geographically distributed, to push forward the grand vision of the servitization of predictive health monitoring methods for large-scale interoperability.

scholarly journals Power Plant Economic Analysis: Maximizing Lifecycle Profitability by Simulating Preliminary Design Solutions of Steam-Cycle Conditions

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2245 ◽  
Author(s):  
Chul-Seung Hong ◽  
Eul-Bum Lee
Keyword(s):  
Power Plant ◽  
Economic Analysis ◽  
Power Efficiency ◽  
The Philippines ◽  
Parametric Estimation ◽  
Design Solution ◽  
Maximum Efficiency ◽  
Technical Factors ◽  
Design Solutions ◽  
Steam Cycle

Many existing financial models for power plants chose a design based on the maximum thermal efficiency excluding the operational (OPEX) and capital (CAPEX) cost variations of technical factors. These factors are often fixed because including them in financial assessments can be burdensome and it is assumed that maximum efficiency equals maximum profit. However, this assumption may not always be right. Through 19,440 power plant steam-cycle design solutions and their associated OPEX and CAPEX, this study found the eighth most thermally-efficient solution to be $1.284 M more profitable than the traditional thermally-optimized design solution. As such, this paper presents a model incorporating technical factors through parametric estimation by minimizing the burden on decision makers. While this may reduce precision, it allows for quick cost assessments across differing design solutions. The data for model development was collected from a Korean-constructed, operational 600 MW coal-fired power plant in the Philippines. Using the Thermoflex software, nearly all design configurations’ heat rate outputs are simulated. Profitability is then optimized based on the resultant design configuration’s impact on revenue and CAPEX and OPEX costs. The simulation inputs included variables found to be most impactful on the steam generated power efficiency per existing literature. Lastly, the model includes an assessment of cost impacts among recent environmental regulations by incorporating carbon tax costs and a sensitivity analysis. The economic analysis model discussed in this paper is non-existent in current literature and will aid the power-plant project investment industry through their project feasibility analyses.

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