고분자 전해질막 연료전지 시스템 고장 반응 및 심각도 기반 고장진단 방법

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부(멀티스케일 기계설계전공), 2021.8. 박진영.최근 들어 지속 가능하며 오염 없는 수소 사회에 대한 관심이 증가하고 있다. 수소는 우주에 가장 많은 물질이며 또한 쉽게 제조할 수 있다. 친환경 기술 개발과 더불어 수소 사회가 실현 된다는 가정하에, 수소에너지를 전기에너지로 변환시켜 주는 장치가 반드시 필요하다. 고분자 전해질막 연료전지 시스템은 산소와 수소의 전기화학반응을 이용하여 전기를 발전시키는 시스템이며, 다른 변환 장치에 비해 많은 장점을 가지고 있다. 또한, 가장 널리 사용되고 있는 장치이기도 하다. 다만, 연료전지의 상용화와 보급에 있어 내구성과 신뢰성은 아직 부족하여 극복해야 할 문제로 언급되고 있다. 이러한 내구성과 신뢰성 증진을 위해서는 고장 진단 기술이 반드시 필요하다. 연료전지는 운전 조건에 따라 그 성능과 내구성이 크게 영향을 받기 때문에 시스템에 발생한 문제를 빠르게 진단하여 장치를 보호하는 것이 중요하기 때문이다. 본 연구에서는 먼저 연료전지 시스템에 고장 발생시 그 영향을 관찰하였다. 일차적으로는 연료전지 스택에 반응의 공급 혹은 냉각이 원활히 이루어지지 않는 상황에서의 변화를 실험적으로 관찰하였다. 이어, 연료전지 시스템을 제작하여 연료 공급 시스템, 공기 공급 시스템, 열 관리 시스템에서 발생할 수 잇는 고장 시나리오를 설정하였다. 고장 시나리오는 연료전지 스택 혹은 시스템 전체에 미칠 수 있는 영향을 그 심각도에 따라 분류하였다. 마지막으로 고장을 인가하고 제어 및 계측 신호의 변화 양상을 관찰 및 분석하였다. 다음으로, 본 연구에서는 이러한 연료전지 시스템 고장을 진단할 수 있는 방법을 제안하였다. 고장의 심각도에 따라 변화 반응의 크기와 속도가 다르다는 점에 착안하여, 치명적 고장, 심각한 고장, 사소한 고장을 각각 진단하는 뉴럴 네트워크 기반 알고리즘을 개발하고, 이를 심각도 기반 고장 진단 알고리즘이라 명명하였다. 각각의 뉴럴 네트워크에 입력하는 계측 잔차 값의 이동 평균 시간과 이를 나누는 분산의 배수 값을 조절함으로 서, 진단 알고리즘의 민감도와 강건성을 동시에 달성할 수 있다는 장점을 갖는다. 또한 고장 실험 없이 고장 시 예상되는 제어 및 계측 신호 값의 증감을 테이블화 하는 것만으로도 본 알고리즘을 개발할 수 있다는 장점을 갖는다. 이러한 방법으로 개발 된 심각도 기반 고장 진단 알고리즘에 고장실험 데이터를 입력한 결과 고장을 성공적으로 진단하는 것을 확인하였다. 이어서, 본 연구에서는 내부 전류 분포를 예측할 수 있는 모델 개발법을 제안하였다. 지금까지의 연료전지 내부 전류 분포 연구는 실험 기반으로, 혹은 모델기반으로 각각 이루어져 왔다. 다만 두가지 접근 방법 모두 필연적으로 한계점을 가진다. 이러한 한계를 뉴럴 네트워크를 도입하여 극복하고자 하였다. 분할 연료전지를 이용하여 압력, 온도, 유량 및 가습도가 변하는 다양한 운전 조건에서의 전류 분포 정보를 습득하였고, 이를 뉴럴 네트워크 모델에 학습시켰다. 그 결과 제한된 데이터만으로 모델을 개발하고, 다양한 운전조건에서 전류 밀도 분포를 예측 할 수 있음을 확인하였다. 이러한 접근법은 상용 연료전지의 개발 과정에서 효율적으로 활용될 것이라 생각한다. 마지막으로, 본 연구에서는 열화 및 고장 발생에 따른 내부 전류 분포를 예측할 수 있는 모델 개발법을 제안하고 검증하였다. 연료전지는 시간이 지나면 필연적으로 열화 한다는 특성을 가지고 있다. 따라서 열화에 따른 전류 분포 변화 특성을 이해하고 예측하는 작업은 중요하다. 하여, 가속 열화 기법을 도입하여 열화에 따른 내부 전류 분포 변화를 먼저 관찰하였다. 또한 가속 열화 시험 중간에 운전 온도 상승, 당량비 증감, 가습도 저하와 같은 고장을 인가하여 전류 분포 변화 정보를 추가적으로 습득하였다. 이러한 정보를 바탕으로 열화 및 열화 진행 상태에서의 고장 발생 시, 전류밀도 분포를 예측하는 뉴럴 네트워크 기반 모델을 개발하였다. 그 결과 모델을 이용하여 효율적이면서도 정확한 전류 밀도 분포 예측이 가능함을 확인하였다.In recent years, interest in hydrogen society has grown from the viewpoint of a sustainable clean energy society. Hydrogen is the most abundant element in the universe and can be easily produced. When hydrogen becomes a commonly used fuel, an energy conversion device is needed. A polymer electrolyte membrane fuel cell (PEMFC) system is the most widely distributed device so far, with many advantages among many devices. However, there still are some barriers to overcome for the commercialization of the PEMFC system; reliability and durability. In order to improve the reliability and durability of the fuel cell system, fault diagnosis technology is essentially required. Since the performance and durability of the PFMFC highly depend on operating conditions, faults in the system should be correctly detected in the early stage for its protection. Firstly, fault responses of a PEMFC stack and PEMFC system are investigated in this study. A response of 1 kW PEMFC stack under insufficient reactant supply or failure thermal management is investigated. Next, probable fault scenarios in a 1 kW class PEMFC system are established. The fault scenarios in air providing system, fuel providing system and thermal management system are classified depending on their fault severity to the stack or the entire system. Responses of control and sensing signals are investigated and analyzed under each fault scenario. Secondly, a fault diagnostic method for the PEMFC system is suggested in this study. Considering that response time and magnitude differ depending on fault severity, three neural networks that diagnose the critical fault, significant fault and minor fault, respectively, are developed. The neural networks together work as a severity-based fault diagnosis algorithm. The algorithm can achieve both sensitivity and robustness by adjusting the moving average time and standard deviation multiplication value that divides the residual data. The residual data is acquired from the control and sensing signals during the system operation. The severity-based fault diagnosis algorithm can be developed using a tabularized expected fault response without experimental data. As a result, the developed algorithm successfully diagnosed all the considered fault scenarios. Thirdly, a local current distribution prediction method is suggested in this study. Local current distribution studies have been conducted experimentally or numerically. Both approaches had limitations. In order to overcome the limitations, a neural network-based local current distribution prediction model is developed. Current distribution data is collected under various pressure, temperature, reactant stoichiometric ratio and relative humidity conditions. The model is developed with the data and successfully predicted local current distribution. Using the model, the effect of the operating parameters is investigated. Lastly, a local current distribution prediction model under degradation and fault is suggested in this study. The performance of the fuel cell inevitably decreases over time. With the degradation, local current distribution also changes. Therefore, understanding and predicting the current distribution changes are important. An accelerated stress test (AST) is applied to the fuel cell for fast degradation. With the AST, current distribution data is collected. Also, fault data under elevated temperature, reduced humidity and varying cathode stoichiometric ratio condition are collected. With the collected data, local current distribution model based on a neural network is developed. As a result, the model predicted the current distribution under degradation and fault with high accuracy. In summary, a fault response of PEMFC is investigated from the viewpoint of the system and local current distribution. A severity-based fault diagnosis algorithm is suggested and validated with the PEMFC system fault experimental data. Also, local current distribution prediction algorithm is suggested and successively predicted the current distribution under PEMFC degradation and faults.Chapter 1. Introduction 1 1.1 Background of study 1 1.2 Literature survey 7 1.2.1 PEMFC fault diagnosis technology 7 1.2.2 PEMFC local current distribution 11 1.3 Objective and scopes 19 Chapter 2. Fault response of PEMFC system 22 2.1 Introduction 22 2.2 Fault response of 1 kW stack 23 2.2.1 Experimental setup description 23 2.2.2 Fault response of stack 29 2.3 Fault response of 1 kW PEMFC system 34 2.3.1 PEMFC system description 34 2.3.2 Fault scenarios 44 2.3.3 Fault response of PEMFC system 52 2.4 Summary 63 Chapter 3. Severity-based fault diagnostic method for PEMFC system 64 3.1 Introduction 64 3.2 Fault residual patterns 68 3.2.1 Input values 68 3.2.2 Normal state 69 3.2.3 Faul residual pattern table 72 3.3 Fault diagnosis algorithm development 79 3.3.1 Severity-based fault diagnosis concept 79 3.3.2 Algorithm development 82 3.4 Results and discussion 88 3.5 Summary 105 Chapter 4. Current distribution prediction with neural network 106 4.1 Introduction 106 4.2 Experimental setup 108 4.2.1 Experimental apparatus 108 4.2.2 Experimental conditions 113 4.3 Model development 116 4.3.1 Neural network model 116 4.3.2 Data conditioning 119 4.3.3 Model training 122 4.4 Results and discussion 127 4.4.1 Model accuracy 127 4.4.2 Effects of parameters on current distribution 129 4.4.3 Effects of parameters on standard deviations 133 4.4.3 Uniform current distribution 134 4.5 Summary 138 Chapter 5. Current distribution prediction under degradation and fault 139 5.1 Introduction 139 5.2 Accelerated stress test 140 5.3 Experimental setup 143 5.3.1 Experimental apparatus 143 5.3.2 Experimental conditions 148 5.4 Current distribution characteristics 152 5.4.1 Local current distribution change with accelerated stress test 152 5.4.2 Local current distribution change under faults 156 5.5 Model development 161 5.5.1 Neural network models 161 5.5.2 Data conditioning 166 5.5.3 Model training 169 5.6 Prediction results 171 5.7 Summary 176 Chapter 6. Concluding remarks 177 References 180 Abstract (in Korean) 197박

Automated Fault Detection, Diagnostics, Impact Evaluation, and Service Decision-Making for Direct Expansion Air Conditioners

This work describes approaches for automatically detecting, diagnosis, and evaluating the impacts of common faults in unitary rooftop air conditioning equipment. A semi-empirical component-based modeling approach using virtual sensors has been implemented using low-cost microcontrollers and tested on fixed-speed and variable-speed equipment using laboratory psychrometric test chambers. A previously developed virtual refrigerant charge sensor was applied to a fixed-speed rooftop unit with combinations of condenser types and expansion valve types and resulted in average prediction errors less than 10%. In addition, a methodology was developed that can be used to tune the empirical parameters of the model using data collected without psychrometric chambers, greatly reducing the experimental effort and costs required for the model. Virtual sensors previously developed for fixed-speed systems were also implemented for a variable-speed rooftop unit without significant loss of accuracy. Much of this work has been devoted to estimating the performance impacts of faults that grow over time, like heat exchanger fouling or refrigerant charge leakage. To estimate these impacts, semi-empirical models for predicting the normal performance of fixed-speed and variable-speed systems have been developed and evaluated using experimentally collected data. In addition, the virtual sensor approaches for estimating the actual performance of systems using low-cost sensor measurements were evaluated. Together, normal performance models and virtual sensor estimations were used to estimate the overall impacts of several faults on system performance. A methodology for quantifying the performance impacts of simultaneously occurring faults has been developed and tested using a detailed system model and experimental results. While relatively simple, simulated and experimentally collected results showed the fault impact models were accurate within 10% of the actual fault impacts. The fault impact evaluation models could be embedded in an AFDD system and used to determine when performance degradation faults should be serviced from an operating cost perspective. In addition, different service and maintenance strategies are compared in this work using a simulation environment that was developed. A data-driven artificial neural network model of a rooftop unit with faults has been derived for this purpose using a detailed fault impact model for direct expansion cooling equipment. This model was coupled with a building model to simulate operating cost impacts of performance degradations and service over the life of cooling equipment. An optimization problem was formulated with the goal to minimize lifetime energy and service costs and was solved using dynamic programming. Using the optimal solution as a baseline, suboptimal service decision-making strategies were implemented and simulated using the building model. It was found that condition-based maintenance strategies using the outputs of automated fault detection and diagnostics tools can significantly reduce lifetime operating costs over periodic service policies

Multi-Modular Integral Pressurized Water Reactor Control and Operational Reconfiguration for a Flow Control Loop

This dissertation focused on the IRIS design since this will likely be one of the designs of choice for future deployment in the U.S and developing countries. With a net 335 MWe output IRIS novel design falls in the “medium” size category and it is a potential candidate for the so called modular reactors, which may be appropriate for base load electricity generation, especially in regions with smaller electricity grids, but especially well suited for more specialized non-electrical energy applications such as district heating and process steam for desalination. The first objective of this dissertation is to evaluate and quantify the performance of a Nuclear Power Plant (NPP) comprised of two IRIS reactor modules operating simultaneously with a common steam header, which in turn is connected to a single turbine, resulting in a steam-mixing control problem with respect to “load-following” scenarios, such as varying load during the day or reduced consumption during the weekend. To solve this problem a single-module IRIS SIMULINK model previously developed by another researcher is modified to include a second module and was used to quantify the responses from both modules. In order to develop research related to instrumentation and control, and equipment and sensor monitoring, the second objective is to build a two-tank multivariate loop in the Nuclear Engineering Department at the University of Tennessee. This loop provides the framework necessary to investigate and test control strategies and fault detection in sensors, equipment and actuators. The third objective is to experimentally develop and demonstrate a fault-tolerant control strategy using this loop. Using six correlated variables in a single-tank configuration, five inferential models and one Auto-Associative Kernel Regression (AAKR) model were developed to detect faults in process sensors. Once detected the faulty measurements were successfully substituted with prediction values, which would provide the necessary flexibility and time to find the source of discrepancy and resolve it, such as in an operating power plant. Finally, using the same empirical models, an actuator failure was simulated and once detected the control was automatically transferred and reconfigured from one tank to another, providing survivability to the system

Analysis, Development And Design For Early Fault Detection And Fire Safety In Lithium-Ion Battery Technology

Energy storage technologies in its natural form play a key role in the electrical infrastructure, renewable and mobility industry. This form includes the material nomenclature for cell. technology, battery module design, Battery enclosure system design, control, and communication strategy, chemistry profile of various cell technologies, formation and formfactors of cell structure, electrical and mechanical properties of a lithium-ion cell, behavior of the cell under high voltage, low voltage, elevated temperature and lower temperature, multiple charging of a lithium-ion batteries. Energy storage industry is growing rapidly, and the industry is experiencing an unprecedented safety concern and issues in terms of fire and explosion at cell and system level. There has been. other research conducted with proposed theories and recommendations to resolve these issues. The failure modes for energy storage systems can be derived using different methodologies such as failure mode effects analysis (FMEA). Early detection mode and strategies in lithium-ion batteries to overcome the failure modes can be caused by endothermic reaction in the cell, further protection. devices, fire inhibition and ventilation. Endothermic safety involves modifications of materials in anode, cathode, and electrolyte. Chemical components added to the battery electrolyte improve the characteristics helping in the improvement of solid-electrolyte interphase and stability. Traditional energy storage system protection device fuse at the cell level, and contactors at the rack level and circuit breakers, current interrupt devices, and positive temperature coefficient devices at the system level. This research will employ classical experimental methods to explore, review and evaluate all the five main energy technologies and narrow down to electrochemical energy storage technologies. with the two main market ready lithium-ion battery technology (LiFePO4/ G and NMC/G) technology cells and why are they valuable in the energy storage and E-mobility space. Also, will focus on the electrical, mechanical design, testing of the battery module into a rack system, advancements in battery chemistries, relevant modes, mechanisms of potential failures, and early detection strategies to overcome these failures. Finally, how the problems of fires, safety concerns and difficulty in transporting already fully assembled energy storage systems can be resolved and be demystified in lithium-ion technology. Keywords Control strategy, Energy storage system, electrolyte, failure mode, early detection, Lithium-Ion cell technology, Battey system