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Records with Subject: Process Monitoring
Showing records 1 to 25 of 60. [First] Page: 1 2 3 Last
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]
Gas-Path Health Estimation for an Aircraft Engine Based on a Sliding Mode Observer
Xiaodong Chang, Jinquan Huang, Feng Lu, Haobo Sun
January 7, 2019 (v1)
Keywords: aircraft engines, health estimation, linear matrix inequalities (LMIs), modeling uncertainties, sliding mode observer (SMO)
Aircraft engine gas-path health monitoring (GPHM) plays a critical role in engine health management (EHM). Among model-based approaches, the Kalman filter (KF) has been widely employed in GPHM. The main shortcoming of KF-based scheme lies in the lack of robustness against uncertainties. To enhance robustness, this paper describes a new GPHM architecture using a sliding mode observer (SMO). The convergence of the error system in uncertainty-existing circumstances is demonstrated, and the proposed method is developed to estimate components’ performance degradations regardless of modeling uncertainties. Simulations using a nonlinear model of a turbofan engine are presented, in which health monitoring problems are handled by the KF and the SMO, respectively. Results indicate the proposed approach possesses better diagnostic performance compared to the KF-based scheme, and the SMO shows its strong robustness and great potential to be applied to GPHM.
Monitoring and Analysing Changes in Temperature and Energy in the Ground with Installed Horizontal Ground Heat Exchangers
Pavel Pauli, Pavel Neuberger, Radomír Adamovský
January 7, 2019 (v1)
Keywords: ground, ground source heat pumps systems, heat pump, horizontal ground heat exchangers, specific energies, specific heat flows, temperatures
The objective of this work was to monitor and analyse temperature changes in the ground with installed linear and Slinky-type horizontal ground heat exchangers (HGHEs), used as low-potential heat pump energy sources. Specific heat flows and specific energies extracted from the ground during the heating season were also measured and compared. The verification results showed that the average daily ground temperatures with the two HGHEs are primarily affected by the temperature of the ambient environment. The ground temperatures were higher than ambient temperature during most of the heating season, were only seldom below zero, and were higher by an average 1.97 ± 0.77 K in the ground with the linear HGHE than in the ground with the Slinky-type HGHE. Additionally, the specific thermal output extracted from the ground by the HGHE was higher by 8.45 ± 16.57 W/m² with the linear system than with the Slinky system. The specific energies extracted from the ground over the whole heating season... [more]
A Run-Time Dynamic Reconfigurable Computing System for Lithium-Ion Battery Prognosis
Shaojun Wang, Datong Liu, Jianbao Zhou, Bin Zhang, Yu Peng
January 7, 2019 (v1)
Keywords: field programmable gate array, lithium-ion battery, relevance vector machine, remaining useful life
As safety and reliability critical components, lithium-ion batteries always require real-time diagnosis and prognosis. This often involves a large amount of computation, which makes diagnosis and prognosis difficult to implement, especially in embedded or mobile applications. To address this issue, this paper proposes a run-time Reconfigurable Computing (RC) system on Field Programmable Gate Array (FPGA) for Relevance Vector Machine (RVM) to realize real-time Remaining Useful Life (RUL) estimation. The system leverages state-of-the-art run-time dynamic partial reconfiguration technology and customized computing circuits to balance the hardware occupation and computing efficiency. Optimal hardware resource consumption is achieved by partitioning the RVM algorithm according to a multi-objective optimization. Moreover, pipelined and parallel computation circuits for kernel function and matrix inverse are proposed on FPGA to further accelerate the computation. Experimental results with two... [more]
EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments
Misael Lopez-Ramirez, Luis Ledesma-Carrillo, Eduardo Cabal-Yepez, Carlos Rodriguez-Donate, Homero Miranda-Vidales, Arturo Garcia-Perez
January 7, 2019 (v1)
Keywords: artificial neural networks, empirical mode decomposition, kurtosis, power quality disturbances, Shannon entropy, skewness
In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal for detecting and classifying a limited number of PQDs with some difficulties at low signal-to-noise ratio (SNR). In this regard, recently proposed methodologies for PQD detection estimate several parameters and apply distinct signal processing techniques to improve the detection of PQD. In this work, a novel methodology that merges empirical mode decomposition (EMD), the moments of a random variable, and an artificial neural network (ANN) is proposed for detecting and classifying different PQD. The proposed method estimates skewness, kurtosis, and Shannon entropy from the EMD of one-phase voltage/current signal. Then, an ANN is in charge of classifying the input signal into one of nine different classes for PQD, receiving these paramete... [more]
A New Fault Diagnosis Algorithm for PMSG Wind Turbine Power Converters under Variable Wind Speed Conditions
Yingning Qiu, Hongxin Jiang, Yanhui Feng, Mengnan Cao, Yong Zhao, Dan Li
January 7, 2019 (v1)
Keywords: fault diagnosis, PMSG wind turbine, power converter, turbulence, wind speed
Although Permanent Magnet Synchronous Generator (PMSG) wind turbines (WTs) mitigate gearbox impacts, they requires high reliability of generators and converters. Statistical analysis shows that the failure rate of direct-drive PMSG wind turbines’ generators and inverters are high. Intelligent fault diagnosis algorithms to detect inverters faults is a premise for the condition monitoring system aimed at improving wind turbines’ reliability and availability. The influences of random wind speed and diversified control strategies lead to challenges for developing intelligent fault diagnosis algorithms for converters. This paper studies open-circuit fault features of wind turbine converters in variable wind speed situations through systematic simulation and experiment. A new fault diagnosis algorithm named Wind Speed Based Normalized Current Trajectory is proposed and used to accurately detect and locate faulted IGBT in the circuit arms. It is compared to direct current monitoring and curre... [more]
On Real-Time Fault Detection in Wind Turbines: Sensor Selection Algorithm and Detection Time Reduction Analysis
Francesc Pozo, Yolanda Vidal, Josep M. Serrahima
January 7, 2019 (v1)
Keywords: FAST, Fault Detection, hypothesis test, principal component analysis, sensor selection
In this paper, we address the problem of real-time fault detection in wind turbines. Starting from a data-driven fault detection method, the contribution of this paper is twofold. First, a sensor selection algorithm is proposed with the goal to reduce the computational effort of the fault detection method. Second, an analysis is performed to reduce the data acquisition time needed by the fault detection method, that is, with the goal of reducing the fault detection time. The proposed methods are tested in a benchmark wind turbine where different actuator and sensor failures are simulated. The results demonstrate the performance and effectiveness of the proposed algorithms that dramatically reduce the number of sensors and the fault detection time.
State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF
Bo Xu, Fangqiang Mu, Guoding Shi, Wei Ji, Huangqiu Zhu
December 3, 2018 (v1)
Keywords: permanent magnet synchronous motor, square root unscented Kalman filter, state estimation
This paper focuses on an improved square root unscented Kalman filter (SRUKF) and its application for rotor speed and position estimation of permanent magnet synchronous motor (PMSM). The approach, which combines the SRUKF and strong tracking filter, uses the minimal skew simplex transformation to reduce the number of the sigma points, and utilizes the square root filtering to reduce computational errors. The time-varying fading factor and softening factor are introduced to self-adjust the gain matrices and the state forecast covariance square root matrix, which can realize the residuals orthogonality and force the SRUKF to track the real state rapidly. The theoretical analysis of the improved SRUKF and implementation details for PMSM state estimation are examined. The simulation results show that the improved SRUKF has higher nonlinear approximation accuracy, stronger numerical stability and computational efficiency, and it is an effective and powerful tool for PMSM state estimation u... [more]
Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries
Zhongwei Deng, Lin Yang, Yishan Cai, Hao Deng
November 28, 2018 (v1)
Keywords: battery management system, fuzzy adaptive extended Kalman filter, intrinsic model error, parameter reliability criterion, state of charge
In the field of state of charge (SOC) estimation, the Kalman filter has been widely used for many years, although its performance strongly depends on the accuracy of the battery model as well as the noise covariance. The Kalman gain determines the confidence coefficient of the battery model by adjusting the weight of open circuit voltage (OCV) correction, and has a strong correlation with the measurement noise covariance (R). In this paper, the online identification method is applied to acquire the real model parameters under different operation conditions. A criterion based on the OCV error is proposed to evaluate the reliability of online parameters. Besides, the equivalent circuit model produces an intrinsic model error which is dependent on the load current, and the property that a high battery current or a large current change induces a large model error can be observed. Based on the above prior knowledge, a fuzzy model is established to compensate the model error through updating... [more]
Application of a Heat Flux Sensor in Wind Power Electronics
Elvira Baygildina, Liudmila Smirnova, Kirill Murashko, Raimo Juntunen, Andrey Mityakov, Mikko Kuisma, Olli Pyrhönen, Pasi Peltoniemi, Katja Hynynen, Vladimir Mityakov, Sergey Sapozhnikov
November 28, 2018 (v1)
Keywords: heat flux sensor, measurement, power electronics, reliability, wind turbine
This paper proposes and investigates the application of the gradient heat flux sensor (GHFS) for measuring the local heat flux in power electronics. Thanks to its thinness, the sensor can be placed between the semiconductor module and the heat sink. The GHFS has high sensitivity and yields direct measurements without an interruption to the normal power device operation, which makes it attractive for power electronics applications. The development of systems for monitoring thermal loading and methods for online detection of degradation and failure of power electronic devices is a topical and crucial task. However, online condition monitoring (CM) methods, which include heat flux sensors, have received little research attention so far. In the current research, an insulated-gate bipolar transistor (IGBT) module-based test setup with the GHFS implemented on the base plate of one of the IGBTs is introduced. The heat flux experiments and the IGBT power losses obtained by simulations show sim... [more]
Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach
Zhi-Xin Yang, Xian-Bo Wang, Jian-Hua Zhong
November 28, 2018 (v1)
Keywords: autoencoder (AE), classification, extreme learning machines (ELM), fault diagnosis, wind turbine
Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extreme learning machines (ELM) in a hierarchical structure, where a forwarding list of ELM layers is concatenated and each of them is processed independently for its corresponding role. The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. An ELM based autoencoder is trained to generate a hidden layer output weight matrix, which is then used to transform the input dataset into a new feature representation. Compared with the t... [more]
Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group
Hua Zhang, Lei Pei, Jinlei Sun, Kai Song, Rengui Lu, Yongping Zhao, Chunbo Zhu, Tiansi Wang
November 27, 2018 (v1)
Keywords: capacity fade, fault simulation, online fault diagnosis, parallel-connected battery group, recursive least squares algorithm with restricted memory and constraint
In a parallel-connected battery group (PCBG), capacity degradation is usually caused by the inconsistency between a faulty cell and other normal cells, and the inconsistency occurs due to two potential causes: an aging inconsistency fault or a loose contacting fault. In this paper, a novel method is proposed to perform online and real-time capacity fault diagnosis for PCBGs. Firstly, based on the analysis of parameter variation characteristics of a PCBG with different fault causes, it is found that PCBG resistance can be taken as an indicator for both seeking the faulty PCBG and distinguishing the fault causes. On one hand, the faulty PCBG can be identified by comparing the PCBG resistance among PCBGs; on the other hand, two fault causes can be distinguished by comparing the variance of the PCBG resistances. Furthermore, for online applications, a novel recursive-least-squares algorithm with restricted memory and constraint (RLSRMC), in which the constraint is added to eliminate the “i... [more]
An Intelligent Sensor for the Ultra-High-Frequency Partial Discharge Online Monitoring of Power Transformers
Jian Li, Xudong Li, Lin Du, Min Cao, Guochao Qian
November 27, 2018 (v1)
Keywords: field programmable gate array (FPGA), high-speed voltage comparator, intelligent sensor, level scanning method, online monitoring, partial discharge (PD), ultra-high-frequency (UHF)
Ultra-high-frequency (UHF) partial discharge (PD) online monitoring is an effective way to inspect potential faults and insulation defects in power transformers. The construction of UHF PD online monitoring system is a challenge because of the high-frequency and wide-frequency band of the UHF PD signal. This paper presents a novel, intelligent sensor for UHF PD online monitoring based on a new method, namely a level scanning method. The intelligent sensor can directly acquire the statistical characteristic quantities and is characterized by low cost, few data to output and transmit, Ethernet functionality, and small size for easy installation. The prototype of an intelligent sensor was made. Actual UHF PD experiments with three typical artificial defect models of power transformers were carried out in a laboratory, and the waveform recording method and intelligent sensor proposed were simultaneously used for UHF PD measurement for comparison. The results show that the proposed intellig... [more]
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