522,799 research outputs found
The Early Light Curve of a Type Ia Supernova 2021hpr in NGC 3147: Progenitor Constraints with the Companion Interaction Model
The progenitor system of Type Ia supernovae (SNe Ia) is expected to be a
close binary system of a carbon/oxygen white dwarf (WD) and a non-degenerate
star or another WD. Here, we present results from a high-cadence monitoring
observation of SN 2021hpr in a spiral galaxy, NGC 3147, and constraints on the
progenitor system based on its early multi-color light curve data. First, we
classify SN 2021hpr as a normal SN Ia from its long-term photometric and
spectroscopic data. More interestingly, we found a significant "early excess"
in the light curve over a simple power-law evolution. The early
light curve evolves from blue to red and blue during the first week. To explain
this, we fitted the early part of -band light curves with a two-component
model of the ejecta-companion interaction and a simple power-law model. The
early excess and its color can be explained by shock cooling emission due to a
companion star having a radius of . We also examined
HST pre-explosion images with no detection of a progenitor candidate,
consistent with the above result. However, we could not detect signs of a
significant amount of the stripped mass from a non-degenerate companion star
( for H emission). The early excess light in
the multi-band light curve supports a non-degenerate companion in the
progenitor system of SN 2021hpr. At the same time, the non-detection of
emission lines opens a door for other methods to explain this event.Comment: 26 pages, 13 figures + appendix, Accepted for publication in Ap
Inferential Modeling and Independent Component Analysis for Redundant Sensor Validation
The calibration of redundant safety critical sensors in nuclear power plants is a manual task that consumes valuable time and resources. Automated, data-driven techniques, to monitor the calibration of redundant sensors have been developed over the last two decades, but have not been fully implemented. Parity space methods such as the Instrumentation and Calibration Monitoring Program (ICMP) method developed by Electric Power Research Institute and other empirical based inferential modeling techniques have been developed but have not become viable options.
Existing solutions to the redundant sensor validation problem have several major flaws that restrict their applications. Parity space method, such as ICMP, are not robust for low redundancy conditions and their operation becomes invalid when there are only two redundant sensors. Empirical based inferential modeling is only valid when intrinsic correlations between predictor variables and response variables remain static during the model training and testing phase. They also commonly produce high variance results and are not the optimal solution to the problem.
This dissertation develops and implements independent component analysis (ICA) for redundant sensor validation. Performance of the ICA algorithm produces sufficiently low residual variance parameter estimates when compared to simple averaging, ICMP, and principal component regression (PCR) techniques. For stationary signals, it can detect and isolate sensor drifts for as few as two redundant sensors. It is fast and can be embedded into a real-time system. This is demonstrated on a water level control system.
Additionally, ICA has been merged with inferential modeling technique such as PCR to reduce the prediction error and spillover effects from data anomalies. ICA is easy to use with, only the window size needing specification.
The effectiveness and robustness of the ICA technique is shown through the use of actual nuclear power plant data. A bootstrap technique is used to estimate the prediction uncertainties and validate its usefulness. Bootstrap uncertainty estimates incorporate uncertainties from both data and the model. Thus, the uncertainty estimation is robust and varies from data set to data set.
The ICA based system is proven to be accurate and robust; however, classical ICA algorithms commonly fail when distributions are multi-modal. This most likely occurs during highly non-stationary transients. This research also developed a unity check technique which indicates such failures and applies other, more robust techniques during transients. For linear trending signals, a rotation transform is found useful while standard averaging techniques are used during general transients
Methods and Systems for Fault Diagnosis in Nuclear Power Plants
This research mainly deals with fault diagnosis in nuclear power plants (NPP), based on a framework that integrates contributions from fault scope identification, optimal sensor placement, sensor validation, equipment condition monitoring, and diagnostic reasoning based on pattern analysis. The research has a particular focus on applications where data collected from the existing SCADA (supervisory, control, and data acquisition) system is not sufficient for the fault diagnosis system. Specifically, the following methods and systems are developed.
A sensor placement model is developed to guide optimal placement of sensors in NPPs. The model includes 1) a method to extract a quantitative fault-sensor incidence matrix for a system; 2) a fault diagnosability criterion based on the degree of singularities of the incidence matrix; and 3) procedures to place additional sensors to meet the diagnosability criterion. Usefulness of the proposed method is demonstrated on a nuclear power plant process control test facility (NPCTF). Experimental results show that three pairs of undiagnosable faults can be effectively distinguished with three additional sensors selected by the proposed model.
A wireless sensor network (WSN) is designed and a prototype is implemented on the NPCTF. WSN is an effective tool to collect data for fault diagnosis, especially for systems where additional measurements are needed. The WSN has distributed data processing and information fusion for fault diagnosis. Experimental results on the NPCTF show that the WSN system can be used to diagnose all six fault scenarios considered for the system.
A fault diagnosis method based on semi-supervised pattern classification is developed which requires significantly fewer training data than is typically required in existing fault diagnosis models. It is a promising tool for applications in NPPs, where it is usually difficult to obtain training data under fault conditions for a conventional fault diagnosis model. The proposed method has successfully diagnosed nine types of faults physically simulated on the NPCTF.
For equipment condition monitoring, a modified S-transform (MST) algorithm is developed by using shaping functions, particularly sigmoid functions, to modify the window width of the existing standard S-transform. The MST can achieve superior time-frequency resolution for applications that involves non-stationary multi-modal signals, where classical methods may fail. Effectiveness of the proposed algorithm is demonstrated using a vibration test system as well as applications to detect a collapsed pipe support in the NPCTF. The experimental results show that by observing changes in time-frequency characteristics of vibration signals, one can effectively detect faults occurred in components of an industrial system.
To ensure that a fault diagnosis system does not suffer from erroneous data, a fault detection and isolation (FDI) method based on kernel principal component analysis (KPCA) is extended for sensor validations, where sensor faults are detected and isolated from the reconstruction errors of a KPCA model. The method is validated using measurement data from a physical NPP.
The NPCTF is designed and constructed in this research for experimental validations of fault diagnosis methods and systems. Faults can be physically simulated on the NPCTF. In addition, the NPCTF is designed to support systems based on different instrumentation and control technologies such as WSN and distributed control systems. The NPCTF has been successfully utilized to validate the algorithms and WSN system developed in this research.
In a real world application, it is seldom the case that one single fault diagnostic scheme can meet all the requirements of a fault diagnostic system in a nuclear power. In fact, the values and performance of the diagnosis system can potentially be enhanced if some of the methods developed in this thesis can be integrated into a suite of diagnostic tools. In such an integrated system, WSN nodes can be used to collect additional data deemed necessary by sensor placement models. These data can be integrated with those from existing SCADA systems for more comprehensive fault diagnosis. An online performance monitoring system monitors the conditions of the equipment and provides key information for the tasks of condition-based maintenance. When a fault is detected, the measured data are subsequently acquired and analyzed by pattern classification models to identify the nature of the fault. By analyzing the symptoms of the fault, root causes of the fault can eventually be identified
Gaussian process models for SCADA data based wind turbine performance/condition monitoring
Wind energy has seen remarkable growth in the past decade, and installed wind turbine capacity is increasing significantly every year around the globe. The presence of an excellent offshore wind resource and the need to reduce carbon emissions from electricity generation are driving policy to increase offshore wind generation capacity in UK waters. Logistic and transport issues make offshore maintenance costlier than onshore and availability correspondingly lower, and as a result, there is a growing interest in wind turbine condition monitoring allowing condition based, rather than corrective or scheduled, maintenance.;Offshore wind turbine manufacturers are constantly increasing the rated size the turbines, and also their hub height in order to access higher wind speeds with lower turbulence. However, such scaling up leads to significant increments in terms of materials for both tower structure and foundations, and also costs required for transportation, installation, and maintenance. Wind turbines are costly affairs that comprise several complex systems connected altogether (e.g., hub, drive shaft, gearbox, generator, yaw system, electric drive and so on).;The unexpected failure of these components can cause significant machine unavailability and/or damage to other components. This ultimately increases the operation and maintenance (O&M) cost and subsequently cost of energy (COE). Therefore, identifying faults at an early stage before catastrophic damage occurs is the primary objective associated with wind turbine condition monitoring.;Existing wind turbine condition monitoring strategies, for example, vibration signal analysis and oil debris detection, require costly sensors. The additional costs can be significant depending upon the number of wind turbines typically deployed in offshore wind farms and also, costly expertise is generally required to interpret the results. By contrast, Supervisory Control and Data Acquisition (SCADA) data analysis based condition monitoring could underpin condition based maintenance with little or no additional cost to the wind farm operator.;A Gaussian process (GP) is a stochastic, nonlinear and nonparametric model whose distribution function is the joint distribution of a collection of random variables; it is widely suitable for classification and regression problems. GP is a machine learning algorithm that uses a measure of similarity between subsequent data points (via covariance functions) to fit and or estimate the future value from a training dataset. GP models have been applied to numerous multivariate and multi-task problems including spatial and spatiotemporal contexts.;Furthermore, GP models have been applied to electricity price and residential probabilistic load forecasting, solar power forecasting. However, the application of GPs to wind turbine condition monitoring has to date been limited and not much explored.;This thesis focuses on GP based wind turbine condition monitoring that utilises data from SCADA systems exclusively. The selection of the covariance function greatly influences GP model accuracy. A comparative analysis of different covariance functions for GP models is presented with an in-depth analysis of popularly used stationary covariance functions. Based on this analysis, a suitable covariance function is selected for constructing a GP model-based fault detection algorithm for wind turbine condition monitoring.;By comparing incoming operational SCADA data, effective component condition indicators can be derived where the reference model is based on SCADA data from a healthy turbine constructed and compared against incoming data from a faulty turbine. In this thesis, a GP algorithm is constructed with suitable covariance function to detect incipient turbine operational faults or failures before they result in catastrophic damage so that preventative maintenance can be scheduled in a timely manner.;In order to judge GP model effectiveness, two other methods, based on binning, have been tested and compared with the GP based algorithm. This thesis also considers a range of critical turbine parameters and their impact on the GP fault detection algorithm.;Power is well known to be influenced by air density, and this is reflected in the IEC Standard air density correction procedure. Hence, the proper selection of an air density correction approach can improve the power curve model. This thesis addresses this, explores the different types of air density correction approach, and suggests the best way to incorporate these in the GP models to improve accuracy and reduce uncertainty.;Finally, a SCADA data based fault detection algorithm is constructed to detect failures caused by the yaw misalignment. Two fault detection algorithms based on IEC binning methods (widely used within the wind industry) are developed to assess the performance of the GP based fault detection algorithm in terms of their capability to detect in advance (and by how much) signs of failure, and also their false positive rate by making use of extensive SCADA data and turbine fault and repair logs.;GP models are robust in identifying early anomalies/failures that cause the wind turbine to underperform. This early detection is helpful in preventing machines to reach the catastrophic stage and allow enough time to undertake scheduled maintenance, which ultimately reduces the O&M, cost and maximises the power performance of wind turbines. Overall, results demonstrate the effectiveness of the GP algorithm in improving the performance of wind turbines through condition monitoring.Wind energy has seen remarkable growth in the past decade, and installed wind turbine capacity is increasing significantly every year around the globe. The presence of an excellent offshore wind resource and the need to reduce carbon emissions from electricity generation are driving policy to increase offshore wind generation capacity in UK waters. Logistic and transport issues make offshore maintenance costlier than onshore and availability correspondingly lower, and as a result, there is a growing interest in wind turbine condition monitoring allowing condition based, rather than corrective or scheduled, maintenance.;Offshore wind turbine manufacturers are constantly increasing the rated size the turbines, and also their hub height in order to access higher wind speeds with lower turbulence. However, such scaling up leads to significant increments in terms of materials for both tower structure and foundations, and also costs required for transportation, installation, and maintenance. Wind turbines are costly affairs that comprise several complex systems connected altogether (e.g., hub, drive shaft, gearbox, generator, yaw system, electric drive and so on).;The unexpected failure of these components can cause significant machine unavailability and/or damage to other components. This ultimately increases the operation and maintenance (O&M) cost and subsequently cost of energy (COE). Therefore, identifying faults at an early stage before catastrophic damage occurs is the primary objective associated with wind turbine condition monitoring.;Existing wind turbine condition monitoring strategies, for example, vibration signal analysis and oil debris detection, require costly sensors. The additional costs can be significant depending upon the number of wind turbines typically deployed in offshore wind farms and also, costly expertise is generally required to interpret the results. By contrast, Supervisory Control and Data Acquisition (SCADA) data analysis based condition monitoring could underpin condition based maintenance with little or no additional cost to the wind farm operator.;A Gaussian process (GP) is a stochastic, nonlinear and nonparametric model whose distribution function is the joint distribution of a collection of random variables; it is widely suitable for classification and regression problems. GP is a machine learning algorithm that uses a measure of similarity between subsequent data points (via covariance functions) to fit and or estimate the future value from a training dataset. GP models have been applied to numerous multivariate and multi-task problems including spatial and spatiotemporal contexts.;Furthermore, GP models have been applied to electricity price and residential probabilistic load forecasting, solar power forecasting. However, the application of GPs to wind turbine condition monitoring has to date been limited and not much explored.;This thesis focuses on GP based wind turbine condition monitoring that utilises data from SCADA systems exclusively. The selection of the covariance function greatly influences GP model accuracy. A comparative analysis of different covariance functions for GP models is presented with an in-depth analysis of popularly used stationary covariance functions. Based on this analysis, a suitable covariance function is selected for constructing a GP model-based fault detection algorithm for wind turbine condition monitoring.;By comparing incoming operational SCADA data, effective component condition indicators can be derived where the reference model is based on SCADA data from a healthy turbine constructed and compared against incoming data from a faulty turbine. In this thesis, a GP algorithm is constructed with suitable covariance function to detect incipient turbine operational faults or failures before they result in catastrophic damage so that preventative maintenance can be scheduled in a timely manner.;In order to judge GP model effectiveness, two other methods, based on binning, have been tested and compared with the GP based algorithm. This thesis also considers a range of critical turbine parameters and their impact on the GP fault detection algorithm.;Power is well known to be influenced by air density, and this is reflected in the IEC Standard air density correction procedure. Hence, the proper selection of an air density correction approach can improve the power curve model. This thesis addresses this, explores the different types of air density correction approach, and suggests the best way to incorporate these in the GP models to improve accuracy and reduce uncertainty.;Finally, a SCADA data based fault detection algorithm is constructed to detect failures caused by the yaw misalignment. Two fault detection algorithms based on IEC binning methods (widely used within the wind industry) are developed to assess the performance of the GP based fault detection algorithm in terms of their capability to detect in advance (and by how much) signs of failure, and also their false positive rate by making use of extensive SCADA data and turbine fault and repair logs.;GP models are robust in identifying early anomalies/failures that cause the wind turbine to underperform. This early detection is helpful in preventing machines to reach the catastrophic stage and allow enough time to undertake scheduled maintenance, which ultimately reduces the O&M, cost and maximises the power performance of wind turbines. Overall, results demonstrate the effectiveness of the GP algorithm in improving the performance of wind turbines through condition monitoring
Electrical Grid Anomaly Detection via Tensor Decomposition
Supervisory Control and Data Acquisition (SCADA) systems often serve as the
nervous system for substations within power grids. These systems facilitate
real-time monitoring, data acquisition, control of equipment, and ensure smooth
and efficient operation of the substation and its connected devices. Previous
work has shown that dimensionality reduction-based approaches, such as
Principal Component Analysis (PCA), can be used for accurate identification of
anomalies in SCADA systems. While not specifically applied to SCADA,
non-negative matrix factorization (NMF) has shown strong results at detecting
anomalies in wireless sensor networks. These unsupervised approaches model the
normal or expected behavior and detect the unseen types of attacks or anomalies
by identifying the events that deviate from the expected behavior. These
approaches; however, do not model the complex and multi-dimensional
interactions that are naturally present in SCADA systems. Differently,
non-negative tensor decomposition is a powerful unsupervised machine learning
(ML) method that can model the complex and multi-faceted activity details of
SCADA events. In this work, we novelly apply the tensor decomposition method
Canonical Polyadic Alternating Poisson Regression (CP-APR) with a probabilistic
framework, which has previously shown state-of-the-art anomaly detection
results on cyber network data, to identify anomalies in SCADA systems. We
showcase that the use of statistical behavior analysis of SCADA communication
with tensor decomposition improves the specificity and accuracy of identifying
anomalies in electrical grid systems. In our experiments, we model real-world
SCADA system data collected from the electrical grid operated by Los Alamos
National Laboratory (LANL) which provides transmission and distribution service
through a partnership with Los Alamos County, and detect synthetically
generated anomalies.Comment: 8 pages, 2 figures. In IEEE Military Communications Conference,
Artificial Intelligence for Cyber Workshop (MILCOM), 202
Diagnosis of a battery energy storage system based on principal component analysis
[EN] This paper proposes the use of principal component analysis (PCA) for the state of health (SOH) diagnosis of a battery energy storage system (BESS) that is operating in a renewable energy laboratory located in Chocó, Colombia. The presented methodology allows the detection of false alarms during the operation of the BESS. The principal component analysis model is applied to a parameter set associated to the capacity, internal resistance and open circuit voltage of a battery energy storage system. The parameters are identified from experimental data collected daily. The PCA model retains the first 5 components that collect 80.25% of the total variability. During the test under real operation contidions, PCA diagnosed a degradation of state of health fastest than the comercial battery controller. A change in the charging modes lead to a battery recovery that was also monitored by the proposed algortihm, and control actions are proposed that lead the BESS to work in normal conditions.The authors would like to acknowledge the research project "Implementacion de un programa de desarrollo e investigacion de energias renovables en el departamento del Choco, BPIN 2013000100285 (in Spanish)" and the Universidad TecnolOgica del Choco (in Spanish). The authors would like to thank the anonymous reviewers as well as the editor for their valuable comments that have greatly improved the final version of the paper.Banguero-Palacios, E.; Correcher Salvador, A.; Pérez-Navarro Gómez, Á.; García Moreno, E.; Aristizabal, A. (2020). Diagnosis of a battery energy storage system based on principal component analysis. Renewable Energy. 146:2438-2449. https://doi.org/10.1016/j.renene.2019.08.064S24382449146Perera, A. T. D., Attalage, R. A., Perera, K. K. C. K., & Dassanayake, V. P. C. (2013). Designing standalone hybrid energy systems minimizing initial investment, life cycle cost and pollutant emission. Energy, 54, 220-230. doi:10.1016/j.energy.2013.03.028Krieger, E. M., Cannarella, J., & Arnold, C. B. (2013). A comparison of lead-acid and lithium-based battery behavior and capacity fade in off-grid renewable charging applications. Energy, 60, 492-500. doi:10.1016/j.energy.2013.08.029Aksakal, C., & Sisman, A. (2018). On the Compatibility of Electric Equivalent Circuit Models for Enhanced Flooded Lead Acid Batteries Based on Electrochemical Impedance Spectroscopy. Energies, 11(1), 118. doi:10.3390/en11010118Dhundhara, S., Verma, Y. P., & Williams, A. (2018). Techno-economic analysis of the lithium-ion and lead-acid battery in microgrid systems. Energy Conversion and Management, 177, 122-142. doi:10.1016/j.enconman.2018.09.030Li, X., Shu, X., Shen, J., Xiao, R., Yan, W., & Chen, Z. (2017). An On-Board Remaining Useful Life Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles. Energies, 10(5), 691. doi:10.3390/en10050691Ariza Chacón, H., Banguero, E., Correcher, A., Pérez-Navarro, Á., & Morant, F. (2018). Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms. Energies, 11(9), 2361. doi:10.3390/en11092361Copetti, J. B., Lorenzo, E., & Chenlo, F. (1993). A general battery model for PV system simulation. Progress in Photovoltaics: Research and Applications, 1(4), 283-292. doi:10.1002/pip.4670010405Guasch, D., & Silvestre, S. (2003). Dynamic battery model for photovoltaic applications. Progress in Photovoltaics: Research and Applications, 11(3), 193-206. doi:10.1002/pip.480Blaifi, S., Moulahoum, S., Colak, I., & Merrouche, W. (2016). An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications. Applied Energy, 169, 888-898. doi:10.1016/j.apenergy.2016.02.062Blaifi, S., Moulahoum, S., Colak, I., & Merrouche, W. (2017). Monitoring and enhanced dynamic modeling of battery by genetic algorithm using LabVIEW applied in photovoltaic system. Electrical Engineering, 100(2), 1021-1038. doi:10.1007/s00202-017-0567-6Gao, Z., Cecati, C., & Ding, S. X. (2015). A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches. IEEE Transactions on Industrial Electronics, 62(6), 3757-3767. doi:10.1109/tie.2015.2417501Ferrer, A. (2007). Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process. Quality Engineering, 19(4), 311-325. doi:10.1080/08982110701621304Jiang, Q., Yan, X., & Zhao, W. (2013). Fault Detection and Diagnosis in Chemical Processes Using Sensitive Principal Component Analysis. Industrial & Engineering Chemistry Research, 52(4), 1635-1644. doi:10.1021/ie3017016Fan, J., & Wang, Y. (2014). Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis. Information Sciences, 259, 369-379. doi:10.1016/j.ins.2013.06.021Garcia-Alvarez, D., Fuente, M. J., & Sainz, G. I. (2012). Fault detection and isolation in transient states using principal component analysis. Journal of Process Control, 22(3), 551-563. doi:10.1016/j.jprocont.2012.01.007Banguero, E., Aristizábal, A. J., & Murillo, W. (2017). A Verification Study for Grid-Connected 20 kW Solar PV System Operating in Chocó, Colombia. Energy Procedia, 141, 96-101. doi:10.1016/j.egypro.2017.11.019Rahman, M. A., Anwar, S., & Izadian, A. (2016). Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method. 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Model-Based Cyber-Security Framework for Nuclear Power Plant
A model-based cyber-security framework has been developed to address the new challenges of cyber threats due to the increasing implementation of digital components in the instrumentation and control (I&C) system of modern nuclear power plants. The framework is developed to detect intrusions to pressurized water reactor (PWR) systems that could result in unnecessary reactor shutdown events due to out-of-range water levels of steam generators.
The generation of potential attack scenarios demonstrated a process for identifying the most susceptible attack pathways and components in the I&C system. It starts with identifying two key I&C divisions of the modern AP1000 design related to the reactor trip functions, protection and safety monitoring system, and plant control system. The attack tree analysis is performed on the steam generator (SG) water level control system using the SAPHIRE 8.0.9 code. To quantify the system susceptibility to cyber-attack events, causing reactor trips, we propose sensitivity metrics to identify the low-order sets of components that may be compromised and the degree of perturbations needed for each component. The multi-path event tree (MPET) structures are developed to efficiently and intuitively display a large number of dominant or risk-significant attack scenarios instead of the traditional event trees representing minimal cut sets.
A reduced order model (ROM) has been developed to efficiently represent the SG dynamics and facilitate the detection of potential cyber-attacks. The dynamic ROM is built on the energy balance equation for a single vertical boiling channel approximating a U-tube steam generator. The ROM provides an essential relationship connecting the reactor power, water level, and feedwater flow rate. An application programming interface (API) for the I&C systems serving as the interface between the RELAP5 system code and the ROM has been developed.
A Kalman filtering based detection method has been proposed, providing optimal tracking of SG water level combining the uncertain simulation results with the observation data subject to statistical fluctuations. An observed plant state with significant deviation from the optimal system projection could then indicate potential intrusions into the system. Finally, a mitigation strategy considering the controller feedback is proposed to avoid the reactor trip due to attack on SG water level sensors. The worst-case attack within this issue space is defined, and the maximum delay time allowed for the mitigation is obtained.PHDNuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162955/1/gjunjie_1.pd
Data management of on-line partial discharge monitoring using wireless sensor nodes integrated with a multi-agent system
On-line partial discharge monitoring has been the subject of significant research in previous years but little work has been carried out with regard to the management of on-site data. To date, on-line partial discharge monitoring within a substation has only been concerned with single plant items, so the data management problem has been minimal. As the age of plant equipment increases, so does the need for condition monitoring to ensure maximum lifespan. This paper presents an approach to the management of partial discharge data through the use of embedded monitoring techniques running on wireless sensor nodes. This method is illustrated by a case study on partial discharge monitoring data from an ageing HVDC reactor
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The role of smart sensor networks for voltage monitoring in smart grids
The large-scale deployment of the Smart Grid paradigm will support the evolution of conventional electrical power systems toward active, flexible and self-healing web energy networks composed of distributed and cooperative energy resources. In a Smart Grid platform, distributed voltage monitoring is one of the main issues to address. In this field, the application of traditional hierarchical monitoring paradigms has some disadvantages that could hinder their application in Smart Grids where the constant growth of grid complexity and the need for massive pervasion of Distribution Generation Systems (DGS) require more scalable, more flexible control and regulation paradigms. To try to overcome these challenges, this paper proposes the concept of a decentralized non-hierarchal voltage monitoring architecture based on intelligent and cooperative smart entities. These devices employ traditional sensors to acquire local bus variables and mutually coupled oscillators to assess the main variables describing the global grid state
On-line transformer condition monitoring through diagnostics and anomaly detection
This paper describes the end-to-end components of an on-line system for diagnostics and anomaly detection. The system provides condition monitoring capabilities for two in- service transmission transformers in the UK. These transformers are nearing the end of their design life, and it is hoped that intensive monitoring will enable them to stay in service for longer. The paper discusses the requirements on a system for interpreting data from the sensors installed on site, as well as describing the operation of specific diagnostic and anomaly detection techniques employed. The system is deployed on a substation computer, collecting and interpreting site data on-line
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