Intelligent Prognostic Framework for Degradation Assessment and Remaining Useful Life Estimation of Photovoltaic Module

All industrial systems and machines are subjected to degradation processes, which can be related to the operating conditions. This degradation can cause unwanted stops at any time and major maintenance work sometimes. The accurate prediction of the remaining useful life (RUL) is an important challenge in condition-based maintenance. Prognostic activity allows estimating the RUL before failure occurs and triggering actions to mitigate faults in time when needed. In this study, a new smart prognostic method for photovoltaic module health degradation was developed based on two approaches to achieve more accurate predictions: online diagnosis and data-driven prognosis. This framework of forecasting integrates the strengths of real-time monitoring in the first approach and relevant vector machine in the second. The results show that the proposed method is plausible due to its good prediction of RUL and can be effectively applied to many systems for monitoring and prognostics

Prognostics and health management of power electronics

Prognostics and health management (PHM) is a major tool enabling systems to evaluate their reliability in real-time operation. Despite ground-breaking advances in most engineering and scientific disciplines during the past decades, reliability engineering has not seen significant breakthroughs or noticeable advances. Therefore, self-awareness of the embedded system is also often required in the sense that the system should be able to assess its own health state and failure records, and those of its main components, and take action appropriately. This thesis presents a radically new prognostics approach to reliable system design that will revolutionise complex power electronic systems with robust prognostics capability enhanced Insulated Gate Bipolar Transistors (IGBT) in applications where reliability is significantly challenging and critical. The IGBT is considered as one of the components that is mainly damaged in converters and experiences a number of failure mechanisms, such as bond wire lift off, die attached solder crack, loose gate control voltage, etc. The resulting effects mentioned are complex. For instance, solder crack growth results in increasing the IGBT’s thermal junction which becomes a source of heat turns to wire bond lift off. As a result, the indication of this failure can be seen often in increasing on-state resistance relating to the voltage drop between on-state collector-emitter. On the other hand, hot carrier injection is increased due to electrical stress. Additionally, IGBTs are components that mainly work under high stress, temperature and power consumptions due to the higher range of load that these devices need to switch. This accelerates the degradation mechanism in the power switches in discrete fashion till reaches failure state which fail after several hundred cycles. To this end, exploiting failure mechanism knowledge of IGBTs and identifying failure parameter indication are background information of developing failure model and prognostics algorithm to calculate remaining useful life (RUL) along with ±10% confidence bounds. A number of various prognostics models have been developed for forecasting time to failure of IGBTs and the performance of the presented estimation models has been evaluated based on two different evaluation metrics. The results show significant improvement in health monitoring capability for power switches.Furthermore, the reliability of the power switch was calculated and conducted to fully describe health state of the converter and reconfigure the control parameter using adaptive algorithm under degradation and load mission limitation. As a result, the life expectancy of devices has been increased. These all allow condition-monitoring facilities to minimise stress levels and predict future failure which greatly reduces the likelihood of power switch failures in the first place

Review of Health Prognostics and Condition Monitoring of Electronic Components

To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted

Proton exchange membrane fuel cell degradation prediction based on Adaptive Neuro-Fuzzy Inference Systems .

International audienceThis paper studies the prediction of the output voltage reduction caused by degradation during nominal operating condition of a PEM fuel cell stack. It proposes a methodology based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) which use as input the measures of the fuel cell output voltage during operation. The paper presents the architecture of the ANFIS and studies the selection of its parameters. As the output voltage cannot be represented as a periodical signal, the paper proposes to predict its temporal variation which is then used to construct the prediction of the output voltage. The paper also proposes to split this signal in two components: normal operation and external perturbations. The second component cannot be predicted and then it is not used to train the ANFIS. The performance of the prediction is evaluated on the output voltage of two fuel cells during a long term operation (1000 hours). Validation results suggest that the proposed technique is well adapted to predict degradation in fuel cell systems

Decision-Making for Utility Scale Photovoltaic Systems: Probabilistic Risk Assessment Models for Corrosion of Structural Elements and a Material Selection Approach for Polymeric Components

abstract: The solar energy sector has been growing rapidly over the past decade. Growth in renewable electricity generation using photovoltaic (PV) systems is accompanied by an increased awareness of the fault conditions developing during the operational lifetime of these systems. While the annual energy losses caused by faults in PV systems could reach up to 18.9% of their total capacity, emerging technologies and models are driving for greater efficiency to assure the reliability of a product under its actual application. The objectives of this dissertation consist of (1) reviewing the state of the art and practice of prognostics and health management for the Direct Current (DC) side of photovoltaic systems; (2) assessing the corrosion of the driven posts supporting PV structures in utility scale plants; and (3) assessing the probabilistic risk associated with the failure of polymeric materials that are used in tracker and fixed tilt systems. As photovoltaic systems age under relatively harsh and changing environmental conditions, several potential fault conditions can develop during the operational lifetime including corrosion of supporting structures and failures of polymeric materials. The ability to accurately predict the remaining useful life of photovoltaic systems is critical for plants ‘continuous operation. This research contributes to the body of knowledge of PV systems reliability by: (1) developing a meta-model of the expected service life of mounting structures; (2) creating decision frameworks and tools to support practitioners in mitigating risks; (3) and supporting material selection for fielded and future photovoltaic systems. The newly developed frameworks were validated by a global solar company.Dissertation/ThesisDoctoral Dissertation Civil and Environmental Engineering 201

Fault Diagnosis and Condition Monitoring of Power Electronic Components Using Spread Spectrum Time Domain Reflectometry (SSTDR) and the Concept of Dynamic Safe Operating Area (SOA)

Title from PDF of title page viewed April 1, 2021Dissertation advisors: Faisal Khan and Yong ZengVitaIncludes bibliographical references ( page 117-132)Thesis (Ph.D.)--School of Computing and Engineering and Department of Mathematics and Statistics. University of Missouri--Kansas City, 2021Fault diagnosis and condition monitoring (CM) of power electronic components with a goal of improving system reliability and availability have been one of the major focus areas in the power electronics field in the last decades. Power semiconductor devices such as metal oxide semiconductor field-effect transistor (MOSFET) and insulated-gate bipolar transistor (IGBT) are considered to be the most fragile element of the power electronic systems and their reliability degrades with time due to mechanical and thermo-electrical stresses, which ultimately leads to a complete failure of the overall power conversion systems. Therefore, it is important to know the present state of health (SOH) of the power devices and the remaining useful life (RUL) of a power converter in order to perform preventive scheduled maintenance, which will eventually lead to increased system availability and reduced cost. In conventional practice, device aging and lifetime prediction techniques rely on the estimation of the meantime to failure (MTTF), a value that represents the expected lifespan of a device. MTTF predicts expected lifespan, but cannot adequately predict failures attributed to unusual circumstances or continuous overstress and premature degradation. This inability is due in large part to the fact that it considers the device safe operating area (SOA) or voltage and current ride-through capability to be independent of SOH. However, we experimentally proved that SOA of any semiconductor device goes down with the increased level of aging, and therefore, the probability of occurrence of over-voltage/current situation increases. As a result, the MTTF of the device as well as the overall converter reliability reduces with aging. That said, device degradation can be estimated by accomplishing an accurate online degradation monitoring tool that will determine the dynamic SOA. The correlation between aging and dynamic SOA gives us the useful remaining life of the device or the availability of a circuit. For this monitoring tool, spread spectrum time domain reflectometry (SSTDR) has been proposed and was successfully implemented in live power converters. In SSTDR, a high-frequency sine-modulated pseudo-noise sequence (SMPNS) is sent through the system, and reflections from age-related impedance discontinuities return to the test end where they are analyzed. In the past, SSTDR has been successfully used for device degradation detection in power converters while running at static conditions. However, the rapid variation in impedance throughout the entire live converter circuit caused by the fast-switching operation makes CM more challenging while using SSTDR. The algorithms and techniques developed in this project have overcome this challenge and demonstrated that the SSTDR test data are consistent with the aging of the power devices and do not affect the switching performance of the modulation process even the test signal is applied across the gate-source interface of the power MOSFET. This implies that the SSTDR technique can be integrated with the gate driver module, thereby creating a new platform for an intelligent gate-driver architecture (IGDA) that enables real-time health monitoring of power devices while performing features offered by a commercially available driver. Another application of SSTDR in power electronic systems is the ground fault prediction and detection technique for PV arrays. Protecting PV arrays from ground faults that lead to fire hazards and power loss is imperative to maintaining safe and effective solar power operations. Unlike many standard detection methods, SSTDR does not depend on fault current, therefore, can be implemented for testing ground faults at night or low illumination. However, wide variation in impedance throughout different materials and interconnections makes fault location more challenging than fault detection. This barrier was surmounted by the SSTDR-based fault detection algorithm developed in this project. The proposed algorithm was accounted for any variation in the number of strings, fault resistance, and the number of faults. In addition to its general utility for fault detection, the proposed algorithm can identify the location of multiple faults using only a single measurement point, thereby working as a preventative measure to protect the entire system at a reduced cost. Within the scope of the research work on SSTDR-based fault diagnosis and CM of power electronic components, a cell-level SOH measurement tool has been proposed that utilizes SSTDR to detect the location and aging of individual degraded cells in a large series-parallel connected Li-ion battery pack. This information of cell level SOH along with the respective cell location is critical to calculating the SOH of a battery pack and its remaining useful lifetime since the initial SOH of Li-ion cells varies under different manufacturing processes and operating conditions, causing them to perform inconsistently and thereby affect the performance of the entire battery pack in real-life applications. Unfortunately, today’s BMS considers the SOH of the entire battery pack/cell string as a single SOH and therefore, cannot monitor the SOH at the cell level. A healthy battery string has a specific impedance between the two terminals, and any aged cell in that string will change the impedance value. Since SSTDR can characterize the impedance change in its propagation path along with its location, it can successfully locate the degraded cell in a large battery pack and thereby, can prevent premature failure and catastrophic danger by performing scheduled maintenance.Introduction -- Background study and literature review -- Fundamentals of Spread Spectrum Time Domain Reflectometry (SSTDR): A new method for testing electronics live -- Accelerated aging test bench: design and implementation -- Condition monitoring of power switching in live power switching devices in live power electronic converters using SSTDR -- An irradiance-independent, robust ground-fault detection scheme for PV arrays based on SSTDR -- Detection of degraded/aged cell in a LI-Ion battery pack using SSTDR -- Dynamiv safe operating area (SOA) of power semiconductor devices -- Conclusion and future researc

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance