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Records with Subject: Process Monitoring
Showing records 1 to 25 of 72. [First] Page: 1 2 3 Last
Concept of Designing Thermal Condition Monitoring System with ZigBee/GSM Communication Link for Distributed Energy Resources Network in Rural and Remote Applications
Emmanuel Kobina Payne, Shulin Lu, Qian Wang, Licheng Wu
August 15, 2019 (v1)
Keywords: distributed energy resources, thermal condition monitoring, wireless sensor networks, ZigBee/GSM communications
Monitoring the thermal behavior of distributed energy resources (DERs) network explores the dualism between thermal effects and electrical power flow. This paper proposes a design concept that monitors thermal conditions of DER grids, using ZigBee/GSM wireless sensor networks (WSNs) for real-time monitoring in rural and remote areas. The concept seeks to improve upon existing designs by integrating composite functions. The functions comprise temperature conditions monitoring, data acquisition, and wireless data transmission including data storage and abnormal conditions alert/notification for control solutions. Thus, the concept determines the thermal impact on the DERs integrated network. WSNs with temperature sensors LM35 are utilized to complement ZigBee and Global System for Mobile Communications (GSM) technologies as a communication assisted link. Temperatures are measured from solar Photovoltaic PV modules, wind turbine, distribution cables, protection control units, and energy s... [more]
Identification of the Thief Zone Using a Support Vector Machine Method
Cheng Fu, Tianyue Guo, Chongjiang Liu, Ying Wang, Bin Huang
August 14, 2019 (v1)
Keywords: correlation analysis, signal-to-noise ratio, support vector machine, thief zone, tracer monitoring
Waterflooding is less effective at expanding reservoir production due to interwell thief zones. The thief zones may form during high water cut periods in the case of interconnected injectors and producers or lead to a total loss of injector fluid. We propose to identify the thief zone by using a support vector machine method. Considering the geological factors and development factors of the formation of the thief zone, the signal-to-noise ratio and correlation analysis method were used to select the relevant evaluation indices of the thief zone. The selected evaluation indices of the thief zone were taken as the input of the support vector machine model, and the corresponding recognition results of the thief zone were taken as the output of the support vector machine model. Through the training and learning of sample sets, the response relationship between thief zone and evaluation indices was determined. This method was used to identify 82 well groups in M oilfield, and the identifica... [more]
Fault Identification Using Fast k-Nearest Neighbor Reconstruction
Zhe Zhou, Zuxin Li, Zhiduan Cai, Peiliang Wang
August 7, 2019 (v1)
Keywords: faulty variable identification, k-nearest neighbor estimation, process monitoring, variable contribution
Data with characteristics like nonlinear and non-Gaussian are common in industrial processes. As a non-parametric method, k-nearest neighbor (kNN) rule has shown its superiority in handling the data set with these complex characteristics. Once a fault is detected, to further identify the faulty variables is useful for finding the root cause and important for the process recovery. Without prior fault information, due to the increasing number of process variables, the existing kNN reconstruction-based identification methods need to exhaust all the combinations of variables, which is extremely time-consuming. Our previous work finds that the variable contribution by kNN (VCkNN), which defined in original variable space, can significantly reduce the ratio of false diagnosis. This reliable ranking of the variable contribution can be used to guide the variable selection in the identification procedure. In this paper, we propose a fast kNN reconstruction method by virtue of the ranking of VCk... [more]
Determination of the Acidity of Waste Cooking Oils by Near Infrared Spectroscopy
Juan Francisco García Martín, María del Carmen López Barrera, Miguel Torres García, Qing-An Zhang, Paloma Álvarez Mateos
July 31, 2019 (v1)
Keywords: free acidity, NIRS, partial least squares, waste cooking oil
Waste cooking oils (WCO) recycling companies usually have economic losses for buying WCO not suitable for biodiesel production, e.g., WCO with high free acidity (FA). For this reason, the determination of FA of WCO by near infrared (NIR) spectroscopy was studied in this work to assess its potential for in situ application. To do this, FA of 45 WCO was measured by the classical titration method, which ranged between 0.15 and 3.77%. Then, the NIR spectra from 800 to 2200 nm of these WCO were acquired, and a partial least squares model was built, relating the NIR spectra to FA values. The accuracy of the model was quite high, providing r2 of 0.970 and a ratio of performance to deviation (RPD) of 4.05. Subsequently, a model using an NIR range similar to that provided by portable NIR spectrometers (950−1650 nm) was built. The performance was lower (r2 = 0.905; RPD = 2.66), but even so, with good accuracy, which demonstrates the potential of NIR spectroscopy for the in situ determination of... [more]
Verifying the Representativeness of Water-Quality Monitoring to Manage Water Levels in the Wicheon River, South Korea
Jung Min Ahn, Yong-Seok Kim
July 31, 2019 (v1)
Keywords: backwater, monitoring networks, river management, South Korea, water quality
Changes in water level between the mainstems and tributaries of a river can create backflow effects that alter the representativeness of water-quality monitoring data. In South Korea, 16 multi-functional weirs intended to manage water levels were constructed on 4 major rivers as part of a restoration project. However, they are causing backwater effects in tributaries that coincide with poorer water-quality measurements at monitoring stations along these tributaries despite there being no change in upstream pollution sources. Therefore, this study developed a new methodology for verifying the representativeness of a water-quality monitoring network via spatiotemporal observations of electrical conductivity, self-organizing maps for monthly pattern analysis, locally weighted scatter plot smoothing for trend analysis, load duration curves, and numerical modeling. This approach was tested on the Wicheon River, a primary tributary of the Nakdong River, because the measured decline in water... [more]
A Comparison of Impedance-Based Fault Location Methods for Power Underground Distribution Systems
Enrique Personal, Antonio García, Antonio Parejo, Diego Francisco Larios, Félix Biscarri, Carlos León
July 29, 2019 (v1)
Keywords: fault location, power delivery, power distribution network, underground distribution system
In the last few decades, the Smart Grid paradigm presence has increased within power systems. These new kinds of networks demand new Operations and Planning approaches, following improvements in the quality of service. In this sense, the role of the Distribution Management System, through its Outage Management System, is essential to guarantee the network reliability. This system is responsible for minimizing the consequences arising from a fault event (or network failure). Obviously, knowing where the fault appears is critical for a good reaction of this system. Therefore, several fault location techniques have been proposed. However, most of them provide individual results, associated with specific testbeds, which make the comparison between them difficult. Due to this, a review of fault location methods has been done in this paper, analyzing them for their use on underground distribution lines. Specifically, this study is focused on an impedance-based method because their requiremen... [more]
Application of Electronic Nose for Evaluation of Wastewater Treatment Process Effects at Full-Scale WWTP
Grzegorz Łagód, Sylwia M. Duda, Dariusz Majerek, Adriana Szutt, Agnieszka Dołhańczuk-Śródka
July 28, 2019 (v1)
Keywords: electronic nose, gas sensor array, multidimensional data analysis, odor nuisances, wastewater quality, wastewater treatment processes
This paper presents the results of studies aiming at the assessment and classification of wastewater using an electronic nose. During the experiment, an attempt was made to classify the medium based on an analysis of signals from a gas sensor array, the intensity of which depended on the levels of volatile compounds in the headspace gas mixture above the wastewater table. The research involved samples collected from the mechanical and biological treatment devices of a full-scale wastewater treatment plant (WWTP), as well as wastewater analysis. The measurements were carried out with a metal-oxide-semiconductor (MOS) gas sensor array, when coupled with a computing unit (e.g., a computer with suitable software for the analysis of signals and their interpretation), it formed an e-nose—that is, an imitation of the mammalian olfactory sense. While conducting the research it was observed that the intensity of signals sent by sensors changed with drops in the level of wastewater pollution; th... [more]
State of Charge and State of Health Estimation of AGM VRLA Batteries by Employing a Dual Extended Kalman Filter and an ARX Model for Online Parameter Estimation
Ngoc-Tham Tran, Abdul Basit Khan, Woojin Choi
July 26, 2019 (v1)
Keywords: auto regressive exogenous (ARX) model, dual extended Kalman filter (DEKF), idle stop-start systems, state of charge, state of health
State of charge (SOC) and state of health (SOH) are key issues for the application of batteries, especially the absorbent glass mat valve regulated lead-acid (AGM VRLA) type batteries used in the idle stop start systems (ISSs) that are popularly integrated into conventional engine-based vehicles. This is due to the fact that SOC and SOH estimation accuracy is crucial for optimizing battery energy utilization, ensuring safety and extending battery life cycles. The dual extended Kalman filter (DEKF), which provides an elegant and powerful solution, is widely applied in SOC and SOH estimation based on a battery parameter model. However, the battery parameters are strongly dependent on operation conditions such as the SOC, current rate and temperature. In addition, battery parameters change significantly over the life cycle of a battery. As a result, many experimental pretests investigating the effects of the internal and external conditions of a battery on its parameters are required, sin... [more]
Detection of Internal Short Circuit in Lithium Ion Battery Using Model-Based Switching Model Method
Minhwan Seo, Taedong Goh, Minjun Park, Gyogwon Koo, Sang Woo Kim
July 26, 2019 (v1)
Keywords: battery safety, internal short circuit resistance, model updating method
Early detection of an internal short circuit (ISCr) in a Li-ion battery can prevent it from undergoing thermal runaway, and thereby ensure battery safety. In this paper, a model-based switching model method (SMM) is proposed to detect the ISCr in the Li-ion battery. The SMM updates the model of the Li-ion battery with ISCr to improve the accuracy of ISCr resistance R I S C f estimates. The open circuit voltage (OCV) and the state of charge (SOC) are estimated by applying the equivalent circuit model, and by using the recursive least squares algorithm and the relation between OCV and SOC. As a fault index, the R I S C f is estimated from the estimated OCVs and SOCs to detect the ISCr, and used to update the model; this process yields accurate estimates of OCV and R I S C f . Then the next R I S C f is estimated and used to update the model iteratively. Simulation data from a MATLAB/Simulink model and experimental data verify that this algor... [more]
Battery Internal Temperature Estimation for LiFePO₄ Battery Based on Impedance Phase Shift under Operating Conditions
Jiangong Zhu, Zechang Sun, Xuezhe Wei, Haifeng Dai
July 26, 2019 (v1)
Keywords: electric vehicles (EVs), impedance, internal temperature estimation, lithium-ion battery, phase shift
An impedance-based temperature estimation method is investigated considering the electrochemical non-equilibrium with short-term relaxation time for facilitating the vehicular application. Generally, sufficient relaxation time is required for battery electrochemical equilibrium before the impedance measurement. A detailed experiment is performed to investigate the regularity of the battery impedance in short-term relaxation time after switch-off current excitation, which indicates that the impedance can be measured and also has systematical decrement with the relaxation time growth. Based on the discussion of impedance variation in electrochemical perspective, as well as the monotonic relationship between impedance phase shift and battery internal temperature in the electrochemical equilibrium state, an exponential equation that accounts for both measured phase shift and relaxation time is established to correct the measuring deviation caused by electrochemical non-equilibrium. Then, a... [more]
Sensor Fault Diagnosis for Aero Engine Based on Online Sequential Extreme Learning Machine with Memory Principle
Junjie Lu, Jinquan Huang, Feng Lu
July 26, 2019 (v1)
Keywords: aero engine, extreme learning machine (ELM), memory principle, online learning, sensor fault diagnosis
The on-board sensor fault detection and isolation (FDI) system is essential to guarantee the reliability and safety of an aero engine. In this paper, a novel online sequential extreme learning machine with memory principle (MOS-ELM) is proposed for detecting, isolating, and reconstructing the fault sensor signal of aero engines. In many practical online applications, the sequentially coming data chunk usually possesses a characteristic of timeliness, and the overdue training data may mislead the subsequent learning process. The proposed MOS-ELM can improve the training process by introducing the concept of memory principle into the online sequential extreme learning machine (OS-ELM) to tackle the timeliness of the data chunk. Simulations on some time series problems and some benchmark databases show that MOS-ELM performs better in generalization performance, stability, and prediction accuracy than OS-ELM. The experiment results of the MOS-ELM-based sensor fault diagnosis system also ve... [more]
A Novel Method for Gas Turbine Condition Monitoring Based on KPCA and Analysis of Statistics T2 and SPE
Li Zeng, Wei Long, Yanyan Li
July 5, 2019 (v1)
Keywords: kernel function, KPCA, SPE statistical model, T2 statistical model
Gas turbines are widely used all over the world, in order to ensure the normal operation of gas turbines, it is necessary to monitor the condition of gas turbine and analyze the tested parameters to find the state information contained in parameters. There is a problem in gas turbine condition monitoring that how to locate the fault accurately if failure occurs. To solve the problem, this paper proposes a method to locate the fault of gas turbine components by evaluating the sensitivity of tested parameters to fault. Firstly, the tested parameters are decomposed by the kernel principal component analysis. Then construct the statistics of T2 and SPE in the principal elements space and residual space, respectively. Furthermore, the thresholds of the statistics must be calculated. The influence of tested parameters on faults is analyzed, and the degree of influence is quantified. The fault location can be realized according to the analysis results. The research results show that the propo... [more]
Profile Monitoring for Autocorrelated Reflow Processes with Small Samples
Shu-Kai S. Fan, Chih-Hung Jen, Jai-Xhing Lee
June 10, 2019 (v1)
Keywords: EWMA control chart, Hotelling’s T2 control chart, polynomial regression model, profile monitoring, sum of sine function
The methodology of profile monitoring combines both the model fitting and statistical process control (SPC) techniques. Over the past ten years, a variety of profile monitoring methods have been proposed and extensively investigated in terms of different process profiles. However, monitoring tasks still exhibit a primary problem in that the errors surrounding the functional relationship are frequently assumed to be independent within every single profile. However, the assumption of independence is an unrealistic assumption in many practical instances. In particular, within-profile autocorrelation often occurs in the profile data. To mitigate the within-profile autocorrelation, a monitoring method incorporating an autoregressive (AR)(1) model to cope with autocorrelation is proposed. In this paper, the reflow process with small samples in surface mount technology (SMT) is investigated. In Phase I, three different process models are compared in combination with the first-order autoregres... [more]
Model-Based Stochastic Fault Detection and Diagnosis of Lithium-Ion Batteries
Jeongeun Son, Yuncheng Du
April 15, 2019 (v1)
Keywords: fault detection and classification, lithium-ion battery, Optimization, polynomial chaos expansion, thermal management, uncertainty analysis
The Lithium-ion battery (Li-ion) has become the dominant energy storage solution in many applications, such as hybrid electric and electric vehicles, due to its higher energy density and longer life cycle. For these applications, the battery should perform reliably and pose no safety threats. However, the performance of Li-ion batteries can be affected by abnormal thermal behaviors, defined as faults. It is essential to develop a reliable thermal management system to accurately predict and monitor thermal behavior of a Li-ion battery. Using the first-principle models of batteries, this work presents a stochastic fault detection and diagnosis (FDD) algorithm to identify two particular faults in Li-ion battery cells, using easily measured quantities such as temperatures. In addition, models used for FDD are typically derived from the underlying physical phenomena. To make a model tractable and useful, it is common to make simplifications during the development of the model, which may con... [more]
Categorization of Failures in Polymer Rapid Tools Used for Injection Molding
Anurag Bagalkot, Dirk Pons, Digby Symons, Don Clucas
April 9, 2019 (v1)
Keywords: additive manufacturing, failure modes, injection molding, rapid tooling
Background—Polymer rapid tooling (PRT) inserts for injection molding (IM) are a cost-effective method for prototyping and low-volume manufacturing. However, PRT inserts lack the robustness of steel inserts, leading to progressive deterioration and failure. This causes quality issues and reduced part numbers. Approach—Case studies were performed on PRT inserts, and different failures were observed over the life of the tool. Parts molded from the tool were examined to further understand the failures, and root causes were identified. Findings—Critical parameters affecting the tool life, and the effect of these parameters on different areas of tool are identified. A categorization of the different failure modes and the underlying mechanisms are presented. The main failure modes are: surface deterioration; surface scalding; avulsion; shear failure; bending failure; edge failure. The failure modes influence each other, and they may be connected in cascade sequences. Originality—The original... [more]
A Transient Fault Recognition Method for an AC-DC Hybrid Transmission System Based on MMC Information Fusion
Jikai Chen, Yanhui Dou, Yang Li, Jiang Li, Guoqing Li
March 26, 2019 (v1)
Keywords: discrete wavelet packet transformation, fault recognition, feature extraction, MMC, Renyi entropy, transient faults
At present, the research is still in the primary stage in the process of fault disturbance energy transfer in the multilevel modular converter based high voltage direct current (HVDC-MMC). An urgent problem is how to extract and analyze the fault features hidden in MMC electrical information in further studies on the HVDC system. Aiming at the above, this article analyzes the influence of AC transient disturbance on electrical signals of MMC. At the same time, it is found that the energy distribution of electrical signals in MMC is different for different arms in the same frequency bands after the discrete wavelet packet transformation (DWPT). Renyi wavelet packet energy entropy (RWPEE) and Renyi wavelet packet time entropy (RWPTE) are proposed and applied to AC transient fault feature extraction from electrical signals in MMC. Using the feature extraction results of Renyi wavelet packet entropy (RWPE), a novel recognition method is put forward to recognize AC transient faults using th... [more]
A Fault-Tolerant Location Approach for Transient Voltage Disturbance Source Based on Information Fusion
Guoqing Weng, Feiteng Huang, Jun Yan, Xiaodong Yang, Youbing Zhang, Haibo He
February 27, 2019 (v1)
Keywords: automatic location, DG integration, direction-judgment, fault tolerance, information fusion, transient voltage disturbance source
This paper proposed a fault-tolerant approach based on information fusion (IF) to automatically locate the transient voltage disturbance source (TVDS) in smart distribution grids. We first defined three credibility factors that will influence the reliability of the direction-judgments at each power quality monitor (PQM). Then we proposed two rules of influence and a verification factor for the distributed generation (DG) integration. Based on the two sets of direction-judgment criteria, a novel decision-making method with fault tolerance based on the IF theory is proposed for automatic location of the TVDS. Three critical schemes, including credibility fusion, conflict weakening, and correction for DG integration, have been integrated in the proposed fusion method, followed by a reliability evaluation of the location results. The proposed approach was validated on the IEEE 13-node test feeder, and the TVDS location results demonstrated the effectiveness and fault tolerance of the IF ba... [more]
Moisture Migration in an Oil-Paper Insulation System in Relation to Online Partial Discharge Monitoring of Power Transformers
Wojciech Sikorski, Krzysztof Walczak, Piotr Przybylek
February 27, 2019 (v1)
Keywords: oil-paper insulation, online monitoring, partial discharge (PD), power transformer, water migration
Most power transformers operating in a power system possess oil-paper insulation. A serious defect of this type of insulation, which is associated with long operation time, is an increase in the moisture content. Moisture introduces a number of threats to proper operation of the transformer, e.g., ignition of partial discharges (PDs). Due to the varying temperature of the insulation system during the unit’s normal operation, a dynamic change (migration of water) takes place, precipitating the oil-paper system from a state of hydrodynamic equilibrium. This causes the PDs to be variable in time, and they may intensify or extinguish. Studies on model objects have been conducted to determine the conditions (temperature, humidity, time) that will have an impact on the ignition and intensity of the observed phenomenon of PDs. The conclusions of this study will have a practical application in the evaluation of measurements conducted in the field, especially in relation to the registration of... [more]
Raman Spectral Characteristics of Oil-Paper Insulation and Its Application to Ageing Stage Assessment of Oil-Immersed Transformers
Jingxin Zou, Weigen Chen, Fu Wan, Zhou Fan, Lingling Du
February 5, 2019 (v1)
Keywords: aging stage, clustering analysis, degree of polymerization, power transformers, principal component analysis, Raman spectroscopy, support vector machine
The aging of oil-paper insulation in power transformers may cause serious power failures. Thus, effective monitoring of the condition of the transformer insulation is the key to prevent major accidents. The purpose of this study was to explore the feasibility of confocal laser Raman spectroscopy (CLRS) for assessing the aging condition of oil-paper insulation. Oil-paper insulation samples were subjected to thermal accelerated ageing at 120 °C for up to 160 days according to the procedure described in the IEEE Guide. Meanwhile, the dimension of the Raman spectrum of the insulation oil was reduced by principal component analysis (PCA). The 160 oil-paper insulation samples were divided into five aging stages as training samples by clustering analysis and with the use of the degree of polymerization of the insulating papers. In addition, the features of the Raman spectrum were used as the inputs of a multi-classification support vector machine. Finally, 105 oil-paper insulation testing sam... [more]
The Coupling Fields Characteristics of Cable Joints and Application in the Evaluation of Crimping Process Defects
Fan Yang, Kai Liu, Peng Cheng, Shaohua Wang, Xiaoyu Wang, Bing Gao, Yalin Fang, Rong Xia, Irfan Ullah
February 5, 2019 (v1)
Keywords: coupling fields, defects characteristics, evaluation of internal defects, power cable joint, stress field, temperature field
The internal defects of cable joints always accelerate the deterioration of insulation, until finally accidents can arise due to the explosion of the joints. The formation process of this damage often involves changes in the electromagnetic, temperature and stress distribution of the cable joint, therefore, it is necessary to analyze the electromagnetic-thermal-mechanical distribution of cable joints. Aiming at solving this problem, the paper sets up a 3-D electromagnetic-thermal-mechanical coupling model of cable joints under crimping process defects. Based on the model, the electromagnetic losses distribution, temperature distribution and stress distribution of a cable joint and body are calculated. Then, the coupling fields characteristics in different contact coefficient k, ambient temperature Tamb and load current I were analyzed, and according to the thermal-mechanical characteristics of a cable joint under internal defects, the temperature difference ΔTf and stress difference Δσ... [more]
A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena
Taichun Qin, Shengkui Zeng, Jianbin Guo, Zakwan Skaf
January 31, 2019 (v1)
Keywords: cycle beginning time, hyperplane shift, lithium-ion batteries, rest time, state of health (SOH), support vector machine
State of health (SOH) prognostics is significant for safe and reliable usage of lithium-ion batteries. To accurately predict regeneration phenomena and improve long-term prediction performance of battery SOH, this paper proposes a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time. In this framework, SOH values of regeneration cycles, the number of cycles in regeneration regions and global degradation trends are extracted from raw SOH time series and predicted respectively, and then the three sets of prediction results are integrated to calculate the final overall SOH prediction values. Regeneration phenomena can be found by support vector machine and hyperplane shift (SVM-HS) model by detecting long beginning time intervals. Gaussian process (GP) model is utilized to predict the global degradation trend, and nonlinear models are utilized to predict the regeneration amplitude and the cycle number o... [more]
A Review of Frequency Response Analysis Methods for Power Transformer Diagnostics
Saleh Alsuhaibani, Yasin Khan, Abderrahmane Beroual, Nazar Hussain Malik
January 31, 2019 (v1)
Keywords: condition assessment, diagnostics, frequency response analysis (FRA), power transformer
Power transformers play a critical role in electric power networks. Such transformers can suffer failures due to multiple stresses and aging. Thus, assessment of condition and diagnostic techniques are of great importance for improving power network reliability and service continuity. Several techniques are available to diagnose the faults within the power transformer. Frequency response analysis (FRA) method is a powerful technique for diagnosing transformer winding deformation and several other types of problems that are caused during manufacture, transportation, installation and/or service life. This paper provides a comprehensive review on FRA methods and their applications in diagnostics and fault identification for power transformers. The paper discusses theory and applications of FRA methods as well as various issues and challenges faced in the application of this method.
State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer
Xiaopeng Tang, Boyang Liu, Furong Gao, Zhou Lv
January 31, 2019 (v1)
Keywords: battery management system (BMS), electronic vehicle, lazy-extended Kalman filter (LEKF), state-of-charge (SOC), tuning-free
A battery’s state-of-charge (SOC) can be used to estimate the mileage an electric vehicle (EV) can travel. It is desirable to make such an estimation not only accurate, but also economical in computation, so that the battery management system (BMS) can be cost-effective in its implementation. Existing computationally-efficient SOC estimation algorithms, such as the Luenberger observer, suffer from low accuracy and require tuning of the feedback gain by trial-and-error. In this study, an algorithm named lazy-extended Kalman filter (LEKF) is proposed, to allow the Luenberger observer to learn periodically from the extended Kalman filter (EKF) and solve the problems, while maintaining computational efficiency. We demonstrated the effectiveness and high performance of LEKF by both numerical simulation and experiments under different load conditions. The results show that LEKF can have 50% less computational complexity than the conventional EKF and a near-optimal estimation error of less th... [more]
A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids
Marco Fagiani, Stefano Squartini, Leonardo Gabrielli, Marco Severini, Francesco Piazza
January 31, 2019 (v1)
Keywords: automatic leakage detection, gas grids, Gaussian mixture model, hidden Markov models, novelty detection, one-class support vector machine, smart water
In the last few years, due to the technological improvement of advanced metering infrastructures, water and natural gas grids can be regarded as smart-grids, similarly to power ones. However, considering the number of studies related to the application of computational intelligence to distribution grids, the gap between power grids and water/gas grids is notably wide. For this purpose, in this paper, a framework for leakage identification is presented. The framework is composed of three sections aimed at the extraction and the selection of features and at the detection of leakages. A variation of the Sequential Feature Selection (SFS) algorithm is used to select the best performing features within a set, including, also, innovative temporal ones. The leakage identification is based on novelty detection and exploits the characterization of a normality model. Three statistical approaches, The Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and One-Class Support Vector Machine (OC... [more]
Design and Implementation of a Test-Bench for Efficiency Measurement of Domestic Induction Heating Appliances
Javier Serrano, Jesús Acero, Rafael Alonso, Claudio Carretero, Ignacio Lope, José Miguel Burdío
January 30, 2019 (v1)
Keywords: efficiency measurement, efficient power transfer, induction heating, measurement station
The operation of a domestic induction cooktop is based on the wireless energy transfer from the inductor to the pot. In such systems, the induction efficiency is defined as the ratio between the power delivered to the pot and the consumed power from the supplying converter. The non-transferred power is dissipated in the inductor, raising its temperature. Most efficiency-measuring methods are based on measuring the effective power (pot) and the total power (converter output). While the converter output power is directly measurable, the measurement of the power dissipation in the pot is usually a cause of inaccuracy. In this work, an alternative method to measure the system’s efficiency is proposed and implemented. The method is based on a pot with a reversible base to which the inductor is attached. In the standard configuration, the inductor is placed below the pot in such a way that the delivered power is used to boil water, and the power losses are dissipated to the air. When the pot... [more]
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